Duplicate from Qinghao/AcmeTrace

Co-authored-by: Qinghao Hu <Qinghao@users.noreply.huggingface.co>

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.lz4 filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
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+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
+*.aac filter=lfs diff=lfs merge=lfs -text
+*.flac filter=lfs diff=lfs merge=lfs -text
+*.mp3 filter=lfs diff=lfs merge=lfs -text
+*.ogg filter=lfs diff=lfs merge=lfs -text
+*.wav filter=lfs diff=lfs merge=lfs -text
+# Image files - uncompressed
+*.bmp filter=lfs diff=lfs merge=lfs -text
+*.gif filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
+*.tiff filter=lfs diff=lfs merge=lfs -text
+# Image files - compressed
+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.jpeg filter=lfs diff=lfs merge=lfs -text
+*.webp filter=lfs diff=lfs merge=lfs -text
+data/job_trace/trace_previous_work/helios_trace.csv filter=lfs diff=lfs merge=lfs -text
+data/job_trace/trace_previous_work/pai_trace.csv filter=lfs diff=lfs merge=lfs -text
+data/job_trace/trace_previous_work/philly_trace.csv filter=lfs diff=lfs merge=lfs -text
+data/job_trace/trace_previous_work/philly_trace_merge_retry.csv filter=lfs diff=lfs merge=lfs -text
+data/job_trace/trace_seren.csv filter=lfs diff=lfs merge=lfs -text
+data/utilization/ipmi/CPU_D_Power.csv filter=lfs diff=lfs merge=lfs -text
+data/utilization/ipmi/GPU_AB_Power.csv filter=lfs diff=lfs merge=lfs -text
+data/utilization/ipmi/GPU_C_Power.csv filter=lfs diff=lfs merge=lfs -text
+data/utilization/kalos/FB_FREE.csv filter=lfs diff=lfs merge=lfs -text
+data/utilization/kalos/FB_USED.csv filter=lfs diff=lfs merge=lfs -text
+data/utilization/kalos/GPU_TEMP.csv filter=lfs diff=lfs merge=lfs -text
+data/utilization/kalos/GPU_UTIL.csv filter=lfs diff=lfs merge=lfs -text
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+data/utilization/kalos/MEM_CLOCK.csv filter=lfs diff=lfs merge=lfs -text
+data/utilization/kalos/MEM_COPY_UTIL.csv filter=lfs diff=lfs merge=lfs -text
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+data/utilization/kalos/NODE_MEMORY_UTILIZATION.csv filter=lfs diff=lfs merge=lfs -text
+data/utilization/kalos/PIPE_TENSOR_ACTIVE.csv filter=lfs diff=lfs merge=lfs -text
+data/utilization/kalos/POWER_USAGE.csv filter=lfs diff=lfs merge=lfs -text
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+data/utilization/kalos/XID_ERRORS.csv filter=lfs diff=lfs merge=lfs -text
+data/utilization/seren/DRAM_ACTIVE.csv filter=lfs diff=lfs merge=lfs -text
+data/utilization/seren/FB_FREE.csv filter=lfs diff=lfs merge=lfs -text
+data/utilization/seren/FB_USED.csv filter=lfs diff=lfs merge=lfs -text
+data/utilization/seren/GPU_TEMP.csv filter=lfs diff=lfs merge=lfs -text
+data/utilization/seren/GPU_UTIL.csv filter=lfs diff=lfs merge=lfs -text
+data/utilization/seren/MEMORY_TEMP.csv filter=lfs diff=lfs merge=lfs -text
+data/utilization/seren/MEM_CLOCK.csv filter=lfs diff=lfs merge=lfs -text
+data/utilization/seren/MEM_COPY_UTIL.csv filter=lfs diff=lfs merge=lfs -text
+data/utilization/seren/NODE_CPU_UTILIZATION.csv filter=lfs diff=lfs merge=lfs -text
+data/utilization/seren/NODE_IB_RECEIVE.csv filter=lfs diff=lfs merge=lfs -text
+data/utilization/seren/NODE_IB_SEND.csv filter=lfs diff=lfs merge=lfs -text
+data/utilization/seren/NODE_MEMORY_UTILIZATION.csv filter=lfs diff=lfs merge=lfs -text
+data/utilization/seren/PIPE_TENSOR_ACTIVE.csv filter=lfs diff=lfs merge=lfs -text
+data/utilization/seren/POWER_USAGE.csv filter=lfs diff=lfs merge=lfs -text
+data/utilization/seren/SM_ACTIVE.csv filter=lfs diff=lfs merge=lfs -text
+data/utilization/seren/SM_OCCUPANCY.csv filter=lfs diff=lfs merge=lfs -text
+data/utilization/seren/XID_ERRORS.csv filter=lfs diff=lfs merge=lfs -text

.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+.pytest_cache/
+*.out
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/en/_build/
+docs/zh_cn/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# test
+test.json
+*.out
+*.inp
+
+# data/job_trace/trace_previous_work
+# data/job_trace/trace_seren.csv
+# data/utilization/ipmi
+# data/utilization/seren
+# data/utilization/kalos

LICENSE.txt

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README.md

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+# Acme Trace
+
+This repository hosts the public releases of Acme traces from the Shanghai AI Lab, encompassing workloads spanning from March 2023 to August 2023. We encourage anyone to use the traces for academic purposes, and if you had any questions, feel free to send an email to us, or file an issue on Github. 
+
+Furthermore, we have conducted a thorough analysis of the Acme workloads, detailed in our NSDI '24 paper titled [Characterization of Large Language Model Development in the Datacenter](https://www.usenix.org/conference/nsdi24/presentation/hu). 
+
+<!-- ### **Note that due to space constraints on GitHub, our cluster utilization files are not hosted here. If you're interested in accessing these files, they are available on HuggingFace (~80GB).** 
+<span style="font-size:20px;font-weight:bold;"> Link:</span> [<img src="https://huggingface.co/datasets/huggingface/badges/raw/main/dataset-on-hf-sm.svg">](https://huggingface.co/datasets/Qinghao/AcmeTrace) -->
+
+<!-- **[Acme Full Dataset](https://huggingface.co/datasets/Qinghao/AcmeTrace)** -->
+
+# Acme Dataset
+
+The main trace characteristics, dataset structure and schema are:
+
+## Main Characteristics:
+*	Full Dataset size: 80GB (on HuggingFace)
+*   Dataset size: 109MB
+*	Duration: 6 months
+*   Number of independent GPU clusters: 2
+*	Total number of jobs: 880,740
+*	Total number of GPU jobs: 470,497
+
+## Dataset Structure
+
+```
+📦AcmeTrace
+ ┣ 📂data
+ ┃ ┣ 📂job_trace 
+ ┃ ┃ ┣ 📂trace_previous_work              (Prior job traces for comparison)
+ ┃ ┃ ┃ ┣ 📜helios_trace.csv
+ ┃ ┃ ┃ ┣ 📜xxx.csv
+ ┃ ┃ ┣ 📜trace_kalos.csv                  (Job trace file, collected from scheduler)
+ ┃ ┃ ┗ 📜trace_seren.csv
+ ┃ ┣ 📂utilization
+ ┃ ┃ ┣ 📂ipmi                             (Power of different server models in Seren, collected from IPMI)
+ ┃ ┃ ┃ ┣ 📜CPU_D_Power.csv
+ ┃ ┃ ┃ ┣ 📜GPU_AB_Power.csv
+ ┃ ┃ ┃ ┗ 📜GPU_C_Power.csv
+ ┃ ┃ ┣ 📂kalos                            (Resource utilization logs, collected from DCGM & Prometheus)
+ ┃ ┃ ┃ ┣ 📜DRAM_ACTIVE.csv
+ ┃ ┃ ┃ ┣ 📜xxx.csv
+ ┃ ┃ ┣ 📂seren
+ ┃ ┃ ┃ ┣ 📜DRAM_ACTIVE.csv
+ ┃ ┃ ┃ ┣ 📜xxx.csv
+ ┃ ┃ ┣ 📂util_pkl                         (Processed pickle files for plotting)
+ ┃ ┃ ┃ ┣ 📜gpu_power_kalos.pkl
+ ┃ ┃ ┃ ┣ 📜xxx.pkl
+ ┃ ┣ 📜cluster_summary.csv
+ ┃ ┣ 📜generate_utilization_pkl.ipynb     (Parse utilization files and generate pickles)
+ ┃ ┗ 📜utils.py
+ ┣ 📂figure                               (Examples of trace visualization)
+ ┃ ┣ 📜bar_job_state.pdf
+ ┃ ┣ 📜xxx.pdf
+ ┣ 📜LICENSE.txt
+ ┣ 📜README.md
+ ┗ 📜analysis.ipynb                       (Scripts for plotting)
+```
+
+## Schema and Description
+
+### 1. Job Trace
+
+#### Description
+
+Provides rich information on all jobs submitted to scheduler in each cluster.
+
++ `trace_seren.csv` Example
+
+| job_id  | user  | node_num | gpu_num | cpu_num | type  | state     | submit_time               | start_time                | end_time                  | duration | queue | gpu_time |
+|---------|-------|----------|---------|---------|-------|-----------|---------------------------|---------------------------|---------------------------|----------|-------|----------|
+| 5778432 | u5907 | 1        | 8       | 128     | Other | FAILED    | 2023-03-01 00:18:22+08:00 | 2023-03-01 00:18:54+08:00 | 2023-03-01 00:20:51+08:00 | 117      | 32    | 936.0    |
+| 5778469 | u5907 | 1        | 8       | 128     | Other | COMPLETED | 2023-03-01 00:23:58+08:00 | 2023-03-01 00:24:11+08:00 | 2023-03-01 01:09:04+08:00 | 2693     | 13    | 21544.0  |
+
+
++ `trace_kalos.csv` Example
+
+| job_id           | user  | node_num | gpu_num | cpu_num | mem_per_pod_GB | shared_mem_per_pod | type  | state     | submit_time               | start_time                | end_time                  | fail_time                 | stop_time                 | duration | queue | gpu_time |
+|------------------|-------|----------|---------|---------|----------------|--------------------|-------|-----------|---------------------------|---------------------------|---------------------------|---------------------------|---------------------------|----------|-------|----------|
+| dlctk696s0jbvitv | uf794 | 8        | 64      | 960     | 1000           | 100.0              | Other | FAILED    | 2023-05-17 11:00:58+00:00 | 2023-05-17 11:01:08+00:00 | 2023-05-17 11:01:16+00:00 | 2023-05-17 11:01:16+00:00 |                           | 18       | 10.0  | 1152.0   |
+| dlc1t2ypl09b8qtp | uf794 | 8        | 64      | 960     | 1000           | 100.0              | Other | CANCELLED | 2023-05-17 11:28:42+00:00 | 2023-05-17 11:28:54+00:00 | 2023-05-17 11:30:04+00:00 |                           | 2023-05-17 11:30:04+00:00 | 82       | 12.0  | 5248.0   |
+
+
+#### Schema
+
+| Field         | Description                                         |
+| ------------- | --------------------------------------------------- |
+| `job_id`      | unique id of the job                |
+| `user`        | hashed id for the user, prefix is '*u*'             |
+| `node_num`    | number of nodes in the job                          |
+| `gpu_num`     | number of GPUs required for the job                 |
+| `cpu_num`     | number of CPUs required for the job                 |
+| `type`     | workload type in LLM development                 |
+| `state`       | the job's status upon termination  <sup>1</sup>     |
+| `submit_time` | the job's submission time                           |
+| `start_time`  | the job's start execution time                      |
+| `end_time`    | the job's termination time                          |
+| `duration`    | total job execution time of the job <sup>2</sup>    |
+| `queue`       | total job queue time of the job <sup>3</sup>        |
+| `gpu_time`       | total GPU resource consumed by the job <sup>4</sup>        |
+
+Only in Kalos:
+| Field         | Description                                         |
+| ------------- | --------------------------------------------------- |
+| `mem_per_pod_GB`      | Pod memory resource configuration               |
+| `shared_mem_per_pod`        | Pod memory resource configuration             |
+| `fail_time`    | the time that failure occurs                          |
+| `stop_time`     | the time that job stops                 |
+
+
+
+#### Notes
+1. A job can end up with one of five statuses: (1) `COMPLETED`: it is finished successfully; (2) `CANCELLED`: it is terminated by the user; (3) `FAILED`: it is terminated due to internal or external errors; (4) `TIMEOUT`: the execution time is out of limit; (5) `NODE_FAIL`: it is terminated due to the node crash. `TIMEOUT` and `NODE_FAIL` are very rare in our traces, and are regarded as failed in our analysis.
+2. Calculated from the difference between `end_time` and `start_time`. (Unit: seconds)
+3. Calculated from the difference between `start_time` and `submit_time`. (Unit: seconds)
+4. Calculated from the product between `duration` and `gpu_num`.
+
+
+### 2. Resource Utilization
+
+
+#### Description
+
+Cluster resource utilization monitoring data, collected from DCGM, IPMI and Prometheus.
+
++ `NODE_CPU_UTILIZATION.csv` Example
+
+| Time                      | 10.140.1.10 | 10.140.1.54 | 10.140.1.90 | 10.140.1.41 | 10.140.1.98 | 10.140.0.166 | 10.140.1.4 | 10.140.1.40 | 10.140.1.134 | 10.140.0.147 | 10.140.1.119 | 10.140.0.184 | 10.140.0.151 | 10.140.0.254 | 10.140.1.83 | 10.140.0.246 | 10.140.1.78 | 10.140.1.103 | 10.140.1.155 | 10.140.1.87 | 10.140.1.106 | 10.140.1.140 | 10.140.1.150 | 10.140.1.107 | 10.140.1.172 | 10.140.1.95 | 10.140.0.146 | 10.140.1.125 | 10.140.1.50 | 10.140.1.112 | 10.140.0.159 | 10.140.0.144 | 10.140.0.215 | 10.140.1.36 | 10.140.1.143 | 10.140.1.147 | 10.140.1.14 | 10.140.1.85 | 10.140.1.56 | 10.140.0.243 | 10.140.0.242 | 10.140.1.63 | 10.140.0.132 | 10.140.0.255 | 10.140.1.59 | 10.140.1.130 | 10.140.0.218 | 10.140.0.220 | 10.140.1.27 | 10.140.1.67 | 10.140.1.136 | 10.140.1.84 | 10.140.0.190 | 10.140.1.121 | 10.140.1.146 | 10.140.1.38 | 10.140.0.232 | 10.140.1.18 | 10.140.1.66 | 10.140.0.205 | 10.140.1.154 | 10.140.1.170 | 10.140.0.179 | 10.140.0.135 | 10.140.1.102 | 10.140.1.72 | 10.140.0.249 | 10.140.1.138 | 10.140.1.24 | 10.140.1.60 | 10.140.1.82 | 10.140.0.233 | 10.140.1.23 | 10.140.0.241 | 10.140.0.248 | 10.140.1.68 | 10.140.1.1 | 10.140.0.219 | 10.140.1.116 | 10.140.0.157 | 10.140.0.178 | 10.140.1.29 | 10.140.1.57 | 10.140.0.163 | 10.140.1.52 | 10.140.1.177 | 10.140.1.11 | 10.140.1.26 | 10.140.1.34 | 10.140.1.92 | 10.140.0.211 | 10.140.0.161 | 10.140.0.131 | 10.140.1.124 | 10.140.0.238 | 10.140.1.44 | 10.140.0.237 | 10.140.1.79 | 10.140.1.17 | 10.140.0.214 | 10.140.1.153 | 10.140.1.117 | 10.140.1.109 | 10.140.0.167 | 10.140.0.207 | 10.140.0.134 | 10.140.1.99 | 10.140.1.31 | 10.140.1.127 | 10.140.0.250 | 10.140.1.139 | 10.140.1.53 | 10.140.1.123 | 10.140.1.77 | 10.140.0.133 | 10.140.0.251 | 10.140.1.55 | 10.140.1.12 | 10.140.1.19 | 10.140.1.47 | 10.140.1.118 | 10.140.1.61 | 10.140.1.110 | 10.140.1.64 | 10.140.1.129 | 10.140.0.217 | 10.140.1.104 | 10.140.0.244 | 10.140.0.213 | 10.140.1.97 | 10.140.0.136 | 10.140.1.22 | 10.140.1.32 | 10.140.1.171 | 10.140.1.151 | 10.140.1.96 | 10.140.1.46 | 10.140.0.158 | 10.140.1.51 | 10.140.1.86 | 10.140.1.30 | 10.140.0.156 | 10.140.1.43 | 10.140.1.74 | 10.140.1.89 | 10.140.1.169 | 10.140.1.80 | 10.140.1.2 | 10.140.1.108 | 10.140.1.93 | 10.140.1.73 | 10.140.0.180 | 10.140.1.71 | 10.140.1.88 | 10.140.0.209 | 10.140.1.81 | 10.140.0.152 | 10.140.1.28 | 10.140.1.58 | 10.140.0.236 | 10.140.0.138 | 10.140.0.149 | 10.140.0.206 | 10.140.1.15 | 10.140.0.240 | 10.140.0.203 | 10.140.1.5 | 10.140.1.37 | 10.140.0.143 | 10.140.0.160 | 10.140.0.252 | 10.140.1.75 | 10.140.1.115 | 10.140.0.247 | 10.140.1.6 | 10.140.1.16 | 10.140.0.216 | 10.140.0.150 | 10.140.1.25 | 10.140.0.208 | 10.140.1.62 | 10.140.1.173 | 10.140.1.137 | 10.140.1.9 | 10.140.1.65 | 10.140.1.111 | 10.140.1.135 | 10.140.1.114 | 10.140.1.132 | 10.140.0.154 | 10.140.0.204 | 10.140.1.91 | 10.140.1.120 | 10.140.1.105 | 10.140.1.131 | 10.140.0.165 | 10.140.0.210 | 10.140.0.148 | 10.140.1.133 | 10.140.0.239 | 10.140.1.13 | 10.140.1.144 | 10.140.0.137 | 10.140.0.234 | 10.140.1.142 | 10.140.1.168 | 10.140.0.235 | 10.140.0.140 | 10.140.1.39 | 10.140.0.153 | 10.140.0.139 | 10.140.1.3 | 10.140.1.7 | 10.140.1.94 | 10.140.1.145 | 10.140.1.149 | 10.140.1.152 | 10.140.1.35 | 10.140.0.141 | 10.140.1.69 | 10.140.1.100 | 10.140.1.126 | 10.140.0.142 | 10.140.0.185 | 10.140.1.42 | 10.140.0.231 | 10.140.0.253 | 10.140.0.212 | 10.140.1.21 | 10.140.1.148 | 10.140.1.49 | 10.140.1.128 | 10.140.0.164 | 10.140.1.70 | 10.140.1.45 | 10.140.0.162 | 10.140.1.101 | 10.140.0.145 | 10.140.1.20 | 10.140.1.176 | 10.140.1.33 | 10.140.1.113 | 10.140.1.122 | 10.140.1.76 | 10.140.1.141 | 10.140.1.8 | 10.140.0.155 | 10.140.1.48 |
+|---------------------------|-------------|-------------|-------------|-------------|-------------|--------------|------------|-------------|--------------|--------------|--------------|--------------|--------------|--------------|-------------|--------------|-------------|--------------|--------------|-------------|--------------|--------------|--------------|--------------|--------------|-------------|--------------|--------------|-------------|--------------|--------------|--------------|--------------|-------------|--------------|--------------|-------------|-------------|-------------|--------------|--------------|-------------|--------------|--------------|-------------|--------------|--------------|--------------|-------------|-------------|--------------|-------------|--------------|--------------|--------------|-------------|--------------|-------------|-------------|--------------|--------------|--------------|--------------|--------------|--------------|-------------|--------------|--------------|-------------|-------------|-------------|--------------|-------------|--------------|--------------|-------------|------------|--------------|--------------|--------------|--------------|-------------|-------------|--------------|-------------|--------------|-------------|-------------|-------------|-------------|--------------|--------------|--------------|--------------|--------------|-------------|--------------|-------------|-------------|--------------|--------------|--------------|--------------|--------------|--------------|--------------|-------------|-------------|--------------|--------------|--------------|-------------|--------------|-------------|--------------|--------------|-------------|-------------|-------------|-------------|--------------|-------------|--------------|-------------|--------------|--------------|--------------|--------------|--------------|-------------|--------------|-------------|-------------|--------------|--------------|-------------|-------------|--------------|-------------|-------------|-------------|--------------|-------------|-------------|-------------|--------------|-------------|------------|--------------|-------------|-------------|--------------|-------------|-------------|--------------|-------------|--------------|-------------|-------------|--------------|--------------|--------------|--------------|-------------|--------------|--------------|------------|-------------|--------------|--------------|--------------|-------------|--------------|--------------|------------|-------------|--------------|--------------|-------------|--------------|-------------|--------------|--------------|------------|-------------|--------------|--------------|--------------|--------------|--------------|--------------|-------------|--------------|--------------|--------------|--------------|--------------|--------------|--------------|--------------|-------------|--------------|--------------|--------------|--------------|--------------|--------------|--------------|-------------|--------------|--------------|------------|------------|-------------|--------------|--------------|--------------|-------------|--------------|-------------|--------------|--------------|--------------|--------------|-------------|--------------|--------------|--------------|-------------|--------------|-------------|--------------|--------------|-------------|-------------|--------------|--------------|--------------|-------------|--------------|-------------|--------------|--------------|-------------|--------------|------------|--------------|-------------|
+| 2023-07-01 08:00:00+08:00 | 8.101       | 7.809       | 8.034       | 0.437       | 0.672       | 8.988        | 8.395      | 8.205       | 8.763        | 2.037        | 6.661        | 9.177        | 9.017        | 8.096        | 14.423      | 8.04         | 0.354       | 0.34         | 0.843        | 8.66        | 0.657        | 8.104        | 0.902        | 7.006        | 0.107        | 8.298       | 8.546        | 6.413        | 8.1         | 6.633        | 8.167        | 9.246        | 9.055        | 2.963       | 7.995        | 0.707        | 8.119       | 10.531      | 6.654       | 7.707        | 4.626        | 0.848       | 25.274       | 7.95         | 8.014       | 7.908        | 9.313        | 9.184        | 7.877       | 0.484       | 8.451        | 6.137       | 0.124        | 6.163        | 0.316        | 8.343       | 9.024        | 7.922       | 8.427       | 0.455        | 67.47        | 0.395        | 7.487        | 9.142        | 7.898        | 8.071       | 7.717        | 0.755        | 7.869       | 8.193       | 8.368       | 8.911        | 8.108       | 7.934        | 8.269        | 8.161       | 8.349      | 9.252        | 6.933        | 4.823        | 7.527        | 8.42        | 7.243       | 9.166        | 8.04        | 0.092        | 7.921       | 8.28        | 8.027       | 0.365       | 8.71         | 9.302        | 0.88         | 8.055        | 8.817        | 8.07        | 9.316        | 8.064       | 8.061       | 9.319        | 7.101        | 5.221        | 7.086        | 7.701        | 9.259        | 8.857        | 5.079       | 7.944       | 8.02         | 8.244        | 8.038        | 8.269       | 5.108        | 6.971       | 1.787        | 8.095        | 8.055       | 8.275       | 8.396       | 7.787       | 6.898        | 8.224       | 16.323       | 0.671       | 8.071        | 9.125        | 8.004        | 7.888        | 8.785        | 5.412       | 0.621        | 8.004       | 7.91        | 6.727        | 10.327       | 0.413       | 8.499       | 7.735        | 8.255       | 8.087       | 8.001       | 5.908        | 8.239       | 8.279       | 7.272       | 0.14         | 8.186       | 0.526      | 6.771        | 6.386       | 6.763       | 7.308        | 6.741       | 8.047       | 8.883        | 7.059       | 8.79         | 7.864       | 8.065       | 9.474        | 0.481        | 9.179        | 9.579        | 8.157       | 9.063        | 7.339        | 8.295      | 6.81        | 9.029        | 9.037        | 8.042        | 0.717       | 6.675        | 7.838        | 8.192      | 8.038       | 9.004        | 8.621        | 8.117       | 8.177        | 22.467      | 0.198        | 3.4          | 8.086      | 7.86        | 6.891        | 4.376        | 7.144        | 5.331        | 8.924        | 7.668        | 0.332       | 7.961        | 7.958        | 8.164        | 5.741        | 8.938        | 8.969        | 6.372        | 8.816        | 8.361       | 12.62        | 9.149        | 9.151        | 8.374        | 8.831        | 9.332        | 9.181        | 8.142       | 8.653        | 1.449        | 8.268      | 8.481      | 8.568       | 0.468        | 59.942       | 66.076       | 8.191       | 8.96         | 8.223       | 0.478        | 8.023        | 9.129        | 9.6          | 8.164       | 9.518        | 8.172        | 9.551        | 8.012       | 14.544       | 8.154       | 8.069        | 9.344        | 0.357       | 8.09        | 0.463        | 8.082        | 7.657        | 8.139       | 0.164        | 8.143       | 6.56         | 6.632        | 8.018       | 8.065        | 8.288      | 8.667        | 8.078       |
+
+
+#### Schema
+
+| Field   | Description                             |
+| ------- | --------------------------------------- |
+| `Time`  | sampling timestamp, interval is 15 seconds             |
+| `10.140.xx.xx` | server ip            |

analysis.ipynb

@@ -0,0 +1,1111 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing import List\n",
+    "import os\n",
+    "import pickle\n",
+    "import squarify\n",
+    "\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "import matplotlib\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.patches as mpatches\n",
+    "from matplotlib.lines import Line2D\n",
+    "\n",
+    "SAVEPATH = \"./figure\"\n",
+    "TRACEPATH = \"./data/job_trace\"\n",
+    "PKLPATH = \"./data/utilization/util_pkl\"\n",
+    "\n",
+    "sns.set_style(\"ticks\")\n",
+    "font = {\n",
+    "    \"font.family\": \"Roboto\",\n",
+    "    \"font.size\": 12,\n",
+    "}\n",
+    "sns.set_style(font)\n",
+    "paper_rc = {\n",
+    "    \"lines.linewidth\": 3,\n",
+    "    \"lines.markersize\": 10,\n",
+    "}\n",
+    "sns.set_context(\"paper\", font_scale=2, rc=paper_rc)\n",
+    "cmp = sns.color_palette(\"tab10\")\n",
+    "\n",
+    "\n",
+    "def autolabel(rects, ax, prec=1):\n",
+    "    \"\"\"Attach a text label above each bar in *rects*, displaying its height.\"\"\"\n",
+    "    for rect in rects:\n",
+    "        height = rect.get_height()\n",
+    "        ax.annotate(\n",
+    "            f\"{height:.{prec}f}\",\n",
+    "            xy=(rect.get_x() + rect.get_width() / 2, height),\n",
+    "            xytext=(0, 3),  # 3 points vertical offset\n",
+    "            textcoords=\"offset points\",\n",
+    "            ha=\"center\",\n",
+    "            va=\"bottom\",\n",
+    "            size=16,\n",
+    "        )\n",
+    "\n",
+    "\n",
+    "def calculate_num_cdf_customized_xaxis(df: pd.DataFrame, x_axis: List, key: str):\n",
+    "    \"\"\"\n",
+    "    Calculate quantity percentile CDF with customized threshold of x-axis, y-axis: 0-100%,\n",
+    "    \"\"\"\n",
+    "    # print(\"Parsing\")\n",
+    "    data = df[[key]].copy()\n",
+    "    data.dropna(inplace=True)\n",
+    "\n",
+    "    y = [len(data[data[key] <= x]) / len(data) * 100 for x in x_axis]\n",
+    "\n",
+    "    return y\n",
+    "\n",
+    "\n",
+    "def calculate_sum_cdf_customized_xaxis(df: pd.DataFrame, x_axis: List, key: str, key_to_time=None):\n",
+    "    \"\"\"\n",
+    "    Calculate sum CDF with customized threshold of x-axis, y-axis: 0-100%,\n",
+    "    \"\"\"\n",
+    "    if key_to_time is not None:\n",
+    "        data = df[[key, key_to_time]].copy()\n",
+    "        data[\"new\"] = data[key] * data[key_to_time]\n",
+    "    else:\n",
+    "        data = df[[key]].copy()\n",
+    "        data[\"new\"] = data[key]\n",
+    "    data.dropna(inplace=True)\n",
+    "    sum = data[\"new\"].sum()\n",
+    "\n",
+    "    y = [data[data[key] <= x][\"new\"].sum() / sum * 100 for x in x_axis]\n",
+    "\n",
+    "    return y\n",
+    "\n",
+    "\n",
+    "if not os.path.exists(SAVEPATH):\n",
+    "    os.makedirs(SAVEPATH)\n",
+    "\n",
+    "\n",
+    "data_seren = pd.read_csv(f\"{TRACEPATH}/trace_seren.csv\")\n",
+    "data_kalos = pd.read_csv(f\"{TRACEPATH}/trace_kalos.csv\")\n",
+    "data_philly = pd.read_csv(f\"{TRACEPATH}/trace_previous_work/philly_trace.csv\")\n",
+    "data_helios = pd.read_csv(f\"{TRACEPATH}/trace_previous_work/helios_trace.csv\")\n",
+    "data_pai = pd.read_csv(f\"{TRACEPATH}/trace_previous_work/pai_trace.csv\")\n",
+    "\n",
+    "# A few further process\n",
+    "data_pai.rename(columns={\"plan_cpu\": \"cpu_num\", \"plan_gpu\": \"gpu_num\", \"wait_time\": \"queue\", \"status\": \"state\"}, inplace=True)\n",
+    "data_pai[[\"cpu_num\", \"gpu_num\"]] /= 100\n",
+    "data_pai[\"state\"] = data_pai[\"state\"].map({\"Failed\": \"FAILED\"})  # Not suitable for final state analysis\n",
+    "data_philly[\"state\"] = data_philly[\"state\"].map({\"Pass\": \"COMPLETED\", \"Failed\": \"FAILED\", \"Killed\": \"CANCELLED\"})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### CDF: GPU Job Duration & Utilization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = [2**i for i in range(0, 22)]\n",
+    "y_gpu_seren = calculate_num_cdf_customized_xaxis(data_seren[data_seren[\"gpu_num\"] > 0], x_axis=x, key=\"duration\")\n",
+    "y_gpu_kalos = calculate_num_cdf_customized_xaxis(data_kalos[data_kalos[\"gpu_num\"] > 0], x_axis=x, key=\"duration\")\n",
+    "y_gpu_philly = calculate_num_cdf_customized_xaxis(data_philly[data_philly[\"gpu_num\"] > 0], x_axis=x, key=\"duration\")\n",
+    "y_gpu_helios = calculate_num_cdf_customized_xaxis(data_helios[data_helios[\"gpu_num\"] > 0], x_axis=x, key=\"duration\")\n",
+    "y_gpu_pai = calculate_num_cdf_customized_xaxis(data_pai[data_pai[\"gpu_num\"] > 0], x_axis=x, key=\"duration\")\n",
+    "\n",
+    "with open(f\"{PKLPATH}/util_gpu_seren.pkl\", \"rb\") as file:\n",
+    "    x1, y1, _, _, _, _, _, _, _, _ = pickle.load(file)\n",
+    "\n",
+    "with open(f\"{PKLPATH}/util_gpu_kalos.pkl\", \"rb\") as file:\n",
+    "    x4, y4, _, _ = pickle.load(file)\n",
+    "\n",
+    "with open(f\"{PKLPATH}/util_gpu_pai.pkl\", \"rb\") as file:  # Collect via Antman\n",
+    "    x2, y2 = pickle.load(file)\n",
+    "\n",
+    "with open(f\"{PKLPATH}/util_gpu_philly.pkl\", \"rb\") as file:\n",
+    "    x3, y3 = pickle.load(file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "linestyles = [\"-\", \"--\", \":\", \":\", \":\"]\n",
+    "grid_params = dict(width_ratios=[1, 1])\n",
+    "fig, (ax1, ax2) = plt.subplots(ncols=2, nrows=1, constrained_layout=True, figsize=(9, 3.75))\n",
+    "\n",
+    "ax1.plot(x, y_gpu_seren, linestyles[0], linewidth=3, alpha=0.9, color=cmp[0], label=\"Seren\")\n",
+    "ax1.plot(x, y_gpu_kalos, linestyles[1], linewidth=3, alpha=0.9, color=cmp[1], label=\"Kalos\")\n",
+    "ax1.plot(x, y_gpu_philly, linestyles[2], linewidth=3, alpha=0.9, color=cmp[2], label=\"Philly\")\n",
+    "ax1.plot(x, y_gpu_helios, linestyles[3], linewidth=3, alpha=0.9, color=cmp[3], label=\"Helios\")\n",
+    "ax1.plot(x, y_gpu_pai, linestyles[3], linewidth=3, alpha=0.9, color=cmp[4], label=\"PAI\")\n",
+    "\n",
+    "ax2.plot(x1, y1, linestyles[0], linewidth=3, alpha=0.9, color=cmp[0], label=\"Seren\")\n",
+    "ax2.plot(x4, y4, linestyles[1], linewidth=3, alpha=0.9, color=cmp[1], label=\"Kalos\")\n",
+    "ax2.plot(x2, y2, linestyles[2], linewidth=3, alpha=0.9, color=cmp[4], label=\"PAI\")\n",
+    "ax2.plot(x3, y3, linestyles[2], linewidth=3, alpha=0.9, color=cmp[2], label=\"Philly\")\n",
+    "\n",
+    "ax1.set_xlabel(f\"(a) GPU Job Duration (s)\")\n",
+    "ax1.set_ylabel(f\"CDF (%)\")\n",
+    "ax1.set_xscale(\"log\")\n",
+    "ax1.set_xticks([1e0, 1e1, 1e2, 1e3, 1e4, 1e5, 1e6])\n",
+    "ax1.set_xlim(1, x[-1])\n",
+    "ax1.set_ylim(-0.5, 100.8)\n",
+    "ax1.grid(linestyle=\":\")\n",
+    "\n",
+    "ax2.set_xlabel(f\"(b) GPU Utilization (%)\")\n",
+    "ax2.set_ylabel(f\"CDF (%)\")\n",
+    "ax2.set_xlim(-0.8, 100.8)\n",
+    "ax2.set_xticks([0, 25, 50, 75, 100])\n",
+    "ax2.set_ylim(0, 100.8)\n",
+    "ax2.grid(linestyle=\":\")\n",
+    "\n",
+    "handles, labels = ax1.get_legend_handles_labels()\n",
+    "fig.legend(handles=handles, labels=labels, ncols=5, bbox_to_anchor=(0.1, 1.145), loc=2, columnspacing=1.5, handletextpad=0.5)\n",
+    "\n",
+    "sns.despine()\n",
+    "fig.savefig(f\"{SAVEPATH}/cdf_job_duration_util.pdf\", bbox_inches=\"tight\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### CDF: GPU Number"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = [i for i in range(0, 1025)]\n",
+    "y_gpu_seren = calculate_num_cdf_customized_xaxis(data_seren[data_seren[\"gpu_num\"] > 0], x_axis=x, key=\"gpu_num\")\n",
+    "y_gpu_kalos = calculate_num_cdf_customized_xaxis(data_kalos[data_kalos[\"gpu_num\"] > 0], x_axis=x, key=\"gpu_num\")\n",
+    "y_gpu_philly = calculate_num_cdf_customized_xaxis(data_philly[data_philly[\"gpu_num\"] > 0], x_axis=x, key=\"gpu_num\")\n",
+    "y_gpu_helios = calculate_num_cdf_customized_xaxis(data_helios[data_helios[\"gpu_num\"] > 0], x_axis=x, key=\"gpu_num\")\n",
+    "y_gpu_pai = calculate_num_cdf_customized_xaxis(data_pai[data_pai[\"gpu_num\"] > 0], x_axis=x, key=\"gpu_num\")\n",
+    "\n",
+    "y_gtime_seren = calculate_sum_cdf_customized_xaxis(\n",
+    "    data_seren[data_seren[\"gpu_num\"] > 0], x_axis=x, key=\"gpu_num\", key_to_time=\"duration\"\n",
+    ")\n",
+    "y_gtime_kalos = calculate_sum_cdf_customized_xaxis(\n",
+    "    data_kalos[data_kalos[\"gpu_num\"] > 0], x_axis=x, key=\"gpu_num\", key_to_time=\"duration\"\n",
+    ")\n",
+    "y_gtime_philly = calculate_sum_cdf_customized_xaxis(\n",
+    "    data_philly[data_philly[\"gpu_num\"] > 0], x_axis=x, key=\"gpu_num\", key_to_time=\"duration\"\n",
+    ")\n",
+    "y_gtime_helios = calculate_sum_cdf_customized_xaxis(\n",
+    "    data_helios[data_helios[\"gpu_num\"] > 0], x_axis=x, key=\"gpu_num\", key_to_time=\"duration\"\n",
+    ")\n",
+    "y_gtime_pai = calculate_sum_cdf_customized_xaxis(\n",
+    "    data_pai[data_pai[\"gpu_num\"] > 0], x_axis=x, key=\"gpu_num\", key_to_time=\"duration\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "linestyles = [\"-\", \"--\", \":\", \":\", \":\"]\n",
+    "grid_params = dict(width_ratios=[1, 1])\n",
+    "fig, (ax1, ax2) = plt.subplots(ncols=2, nrows=1, constrained_layout=True, figsize=(9, 3.75))\n",
+    "\n",
+    "ax1.plot(x, y_gpu_seren, linestyles[0], linewidth=3, alpha=0.9, color=cmp[0], label=\"Seren\")\n",
+    "ax1.plot(x, y_gpu_kalos, linestyles[1], linewidth=3, alpha=0.9, color=cmp[1], label=\"Kalos\")\n",
+    "ax1.plot(x, y_gpu_philly, linestyles[2], linewidth=3, alpha=0.9, color=cmp[2], label=\"Philly\")\n",
+    "ax1.plot(x, y_gpu_helios, linestyles[3], linewidth=3, alpha=0.9, color=cmp[3], label=\"Helios\")\n",
+    "ax1.plot(x, y_gpu_pai, linestyles[3], linewidth=3, alpha=0.9, color=cmp[4], label=\"PAI\")\n",
+    "\n",
+    "ax2.plot(x, y_gtime_seren, linestyles[0], linewidth=3, alpha=0.9, color=cmp[0], label=\"Seren\")\n",
+    "ax2.plot(x, y_gtime_kalos, linestyles[1], linewidth=3, alpha=0.9, color=cmp[1], label=\"Kalos\")\n",
+    "ax2.plot(x, y_gtime_philly, linestyles[2], linewidth=3, alpha=0.9, color=cmp[2], label=\"Philly\")\n",
+    "ax2.plot(x, y_gtime_helios, linestyles[3], linewidth=3, alpha=0.9, color=cmp[3], label=\"Helios\")\n",
+    "ax2.plot(x, y_gtime_pai, linestyles[3], linewidth=3, alpha=0.9, color=cmp[4], label=\"PAI\")\n",
+    "\n",
+    "\n",
+    "ax1.set_xlabel(f\"(a) Number of GPU\")\n",
+    "ax1.set_ylabel(f\"CDF of Jobs (%)\")\n",
+    "ax1.set_xscale(\"log\", base=2)\n",
+    "ax1.set_xticks([2**i for i in range(0, 11, 2)])\n",
+    "ax1.set_xticklabels(\n",
+    "    [2**i for i in range(0, 10, 2)]\n",
+    "    + [\n",
+    "        \"1024+\",\n",
+    "    ]\n",
+    ")\n",
+    "ax1.set_xlim(1, x[-1] + 1)\n",
+    "ax1.set_ylim(-0.5, 100.8)\n",
+    "ax1.grid(linestyle=\":\")\n",
+    "\n",
+    "ax2.set_xlabel(f\"(b) Number of GPU\")\n",
+    "ax2.set_ylabel(f\"CDF of GPU Time (%)\")\n",
+    "ax2.set_xscale(\"log\", base=2)\n",
+    "ax2.set_xticks([2**i for i in range(0, 11, 2)])\n",
+    "ax2.set_xticklabels(\n",
+    "    [2**i for i in range(0, 10, 2)]\n",
+    "    + [\n",
+    "        \"1024+\",\n",
+    "    ]\n",
+    ")\n",
+    "ax2.set_xlim(1, x[-1] + 50)\n",
+    "ax2.set_ylim(-0.5, 100.8)\n",
+    "ax2.grid(linestyle=\":\")\n",
+    "\n",
+    "handles, labels = ax1.get_legend_handles_labels()\n",
+    "fig.legend(handles=handles, labels=labels, ncols=5, bbox_to_anchor=(0.1, 1.145), loc=2, columnspacing=1.5, handletextpad=0.5)\n",
+    "\n",
+    "sns.despine()\n",
+    "fig.savefig(f\"{SAVEPATH}/cdf_job_gpunum.pdf\", bbox_inches=\"tight\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Bar: Job Final State"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv(\"./data/cluster_summary.csv\", index_col=\"id\")\n",
+    "grid_params = dict(width_ratios=[1, 1])\n",
+    "fig, (ax1, ax2) = plt.subplots(ncols=2, nrows=1, constrained_layout=True, figsize=(9, 3.75))\n",
+    "\n",
+    "x = np.arange(1, 3)\n",
+    "width = 0.22\n",
+    "p1 = ax1.bar(\n",
+    "    x - width,\n",
+    "    df.loc[[\"Seren\", \"Kalos\"], \"complete_rate_gpu\"] * 100,\n",
+    "    width,\n",
+    "    label=\"Completed\",\n",
+    "    alpha=0.8,\n",
+    "    linewidth=1,\n",
+    "    edgecolor=\"k\",\n",
+    ")\n",
+    "p2 = ax1.bar(\n",
+    "    x, df.loc[[\"Seren\", \"Kalos\"], \"cancel_rate_gpu\"] * 100, width, label=\"Canceled\", alpha=0.8, linewidth=1, edgecolor=\"k\"\n",
+    ")\n",
+    "p3 = ax1.bar(\n",
+    "    x + width, df.loc[[\"Seren\", \"Kalos\"], \"fail_rate_gpu\"] * 100, width, label=\"Failed\", alpha=0.8, linewidth=1, edgecolor=\"k\"\n",
+    ")\n",
+    "\n",
+    "p4 = ax2.bar(\n",
+    "    x - width,\n",
+    "    df.loc[[\"Seren\", \"Kalos\"], \"complete_rate_gpu_time\"] * 100,\n",
+    "    width,\n",
+    "    label=\"Completed\",\n",
+    "    alpha=0.8,\n",
+    "    linewidth=1,\n",
+    "    edgecolor=\"k\",\n",
+    ")\n",
+    "p5 = ax2.bar(\n",
+    "    x, df.loc[[\"Seren\", \"Kalos\"], \"cancel_rate_gpu_time\"] * 100, width, label=\"Canceled\", alpha=0.8, linewidth=1, edgecolor=\"k\"\n",
+    ")\n",
+    "p6 = ax2.bar(\n",
+    "    x + width,\n",
+    "    df.loc[[\"Seren\", \"Kalos\"], \"fail_rate_gpu_time\"] * 100,\n",
+    "    width,\n",
+    "    label=\"Failed\",\n",
+    "    alpha=0.8,\n",
+    "    linewidth=1,\n",
+    "    edgecolor=\"k\",\n",
+    ")\n",
+    "\n",
+    "autolabel(p1, ax1)\n",
+    "autolabel(p2, ax1)\n",
+    "autolabel(p3, ax1)\n",
+    "autolabel(p4, ax2)\n",
+    "autolabel(p5, ax2)\n",
+    "autolabel(p6, ax2)\n",
+    "\n",
+    "ax1.set_xlabel(f\"(a) Job Count\")\n",
+    "ax1.set_ylabel(f\"Fraction (%)\")\n",
+    "ax1.set_xticks(x)\n",
+    "ax1.set_xticklabels([\"Seren\", \"Kalos\"])\n",
+    "ax1.set_xlim(0.5, 2.5)\n",
+    "ax1.set_ylim(0, 100)\n",
+    "ax1.grid(axis=\"y\", linestyle=\":\")\n",
+    "\n",
+    "ax2.set_xlabel(f\"(b) GPU Time\")\n",
+    "ax2.set_ylabel(f\"Fraction (%)\")\n",
+    "ax2.set_xticks(x)\n",
+    "ax2.set_xticklabels([\"Seren\", \"Kalos\"])\n",
+    "ax2.set_xlim(0.5, 2.5)\n",
+    "ax2.set_ylim(0, 100)\n",
+    "ax2.grid(axis=\"y\", linestyle=\":\")\n",
+    "\n",
+    "handles, labels = ax1.get_legend_handles_labels()\n",
+    "fig.legend(handles=handles, labels=labels, ncols=5, bbox_to_anchor=(0.18, 1.145), loc=2)\n",
+    "\n",
+    "sns.despine()\n",
+    "fig.savefig(f\"{SAVEPATH}/bar_job_state.pdf\", bbox_inches=\"tight\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Treemap: Job Number Distribution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"Processing Seren\")\n",
+    "datas = data_seren[data_seren[\"gpu_num\"] > 0]\n",
+    "\n",
+    "job_type = [\"Eval\", \"Pretrain\", \"SFT\", \"MLLM\", \"Debug\", \"Other\"]\n",
+    "df = pd.DataFrame(index=job_type, columns=[\"job_count\"]).fillna(0)\n",
+    "df[\"job_count\"] = df.index.map(datas.groupby(\"type\").size()).astype(int)\n",
+    "df[\"gtime\"] = df.index.map(datas.groupby(\"type\")[\"gpu_time\"].sum()).astype(int)\n",
+    "\n",
+    "total = df[\"job_count\"].sum()\n",
+    "total_gtime = df[\"gtime\"].sum()\n",
+    "\n",
+    "df[\"count_percent\"] = df[\"job_count\"] / total * 100\n",
+    "df[\"gtime_percent\"] = df[\"gtime\"] / total_gtime * 100\n",
+    "\n",
+    "# For plotting\n",
+    "df[\"label\"] = [x + f\"\\n{df.at[x, 'count_percent']:.1f}%\" for x in list(df.index)]\n",
+    "df[\"label_gtime\"] = [x + f\"\\n{df.at[x, 'gtime_percent']:.1f}%\" for x in list(df.index)]\n",
+    "df[\"label_percent\"] = [f\"{df.at[x, 'count_percent']:.1f}%\" for x in list(df.index)]\n",
+    "df[\"label_gtime_percent\"] = [f\"{df.at[x, 'gtime_percent']:.1f}%\" for x in list(df.index)]\n",
+    "df_s = df.copy()\n",
+    "\n",
+    "print(\"Processing Kalos\")\n",
+    "datak = data_kalos[data_kalos[\"gpu_num\"] > 0]\n",
+    "\n",
+    "job_type = [\"Eval\", \"Pretrain\", \"Debug\", \"Other\"]\n",
+    "df = pd.DataFrame(index=job_type, columns=[\"job_count\"]).fillna(0)\n",
+    "df[\"job_count\"] = df.index.map(datak.groupby(\"type\").size()).astype(int)\n",
+    "df[\"gtime\"] = df.index.map(datak.groupby(\"type\")[\"gpu_time\"].sum()).astype(int)\n",
+    "\n",
+    "\n",
+    "total = df[\"job_count\"].sum()\n",
+    "total_gtime = df[\"gtime\"].sum()\n",
+    "\n",
+    "df[\"count_percent\"] = df[\"job_count\"] / total * 100\n",
+    "df[\"gtime_percent\"] = df[\"gtime\"] / total_gtime * 100\n",
+    "\n",
+    "# For plotting\n",
+    "df[\"label\"] = [x + f\"\\n{df.at[x, 'count_percent']:.1f}%\" for x in list(df.index)]\n",
+    "df[\"label_gtime\"] = [x + f\"\\n{df.at[x, 'gtime_percent']:.1f}%\" for x in list(df.index)]\n",
+    "df[\"label_percent\"] = [f\"{df.at[x, 'count_percent']:.1f}\\n%\" for x in list(df.index)]\n",
+    "df[\"label_gtime_percent\"] = [f\"{df.at[x, 'gtime_percent']:.1f}\\n%\" for x in list(df.index)]\n",
+    "df_k = df.copy()\n",
+    "\n",
+    "# For plotting\n",
+    "df_k.at[\"Pretrain\", \"label_gtime_percent\"] = df_k.at[\"Pretrain\", \"label_gtime\"]\n",
+    "df_k.at[\"Eval\", \"label_percent\"] = df_k.at[\"Eval\", \"label\"]\n",
+    "df_k.at[\"Other\", \"label_percent\"] = \"\"\n",
+    "df_k.at[\"Eval\", \"label_gtime_percent\"] = \" \"\n",
+    "\n",
+    "df_s.at[\"Pretrain\", \"label_gtime_percent\"] = df_s.at[\"Pretrain\", \"label_gtime\"]\n",
+    "df_s.at[\"Eval\", \"label_percent\"] = df_s.at[\"Eval\", \"label\"]\n",
+    "df_s.at[\"SFT\", \"label_percent\"] = df_s.at[\"SFT\", \"label\"]\n",
+    "df_s.at[\"Other\", \"label_percent\"] = df_s.at[\"Other\", \"label\"]\n",
+    "df_s.at[\"Pretrain\", \"label_percent\"] = \" \"\n",
+    "df_s.at[\"Debug\", \"label_gtime_percent\"] = df_s.at[\"Debug\", \"label_gtime_percent\"].replace(\"%\", \"\\n%\")\n",
+    "\n",
+    "\n",
+    "cmp_treemap = sns.color_palette(\"pastel\")\n",
+    "label = df_s.index.to_list()\n",
+    "df_s[\"color\"] = cmp_treemap[: len(df_s)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(ncols=2, nrows=2, constrained_layout=True, figsize=(9, 4.2))\n",
+    "FONT = 15\n",
+    "\n",
+    "###### Fig 1 ######\n",
+    "df_s.sort_values(by=\"count_percent\", ascending=False, inplace=True)\n",
+    "squarify.plot(\n",
+    "    ax=ax1,\n",
+    "    sizes=list(df_s[\"job_count\"].values),\n",
+    "    label=df_s[\"label_percent\"],\n",
+    "    text_kwargs={\"fontsize\": FONT},\n",
+    "    color=df_s[\"color\"],\n",
+    "    bar_kwargs={\"alpha\": 0.8, \"linewidth\": 1, \"edgecolor\": \"k\"},\n",
+    ")\n",
+    "\n",
+    "\n",
+    "handles, labels = ax1.get_legend_handles_labels()\n",
+    "handles_new = [handles[0], handles[-1], handles[1], handles[3], handles[4], handles[2]]\n",
+    "fig.legend(\n",
+    "    handles=handles_new, labels=label, ncols=6, bbox_to_anchor=(0.0, 1.135), loc=2, columnspacing=0.82, handletextpad=0.2\n",
+    ")\n",
+    "\n",
+    "###### Fig 2 ######\n",
+    "df_s.sort_values(by=\"gtime\", ascending=False, inplace=True)\n",
+    "squarify.plot(\n",
+    "    ax=ax2,\n",
+    "    sizes=list(df_s[\"gtime\"].values),\n",
+    "    label=df_s[\"label_gtime_percent\"],\n",
+    "    text_kwargs={\"fontsize\": FONT},\n",
+    "    color=df_s[\"color\"],\n",
+    "    bar_kwargs={\"alpha\": 0.8, \"linewidth\": 1, \"edgecolor\": \"k\"},\n",
+    ")\n",
+    "\n",
+    "plt.tick_params(axis=\"both\", which=\"both\", bottom=False, top=False, left=False, right=False)\n",
+    "\n",
+    "ax1.set_xlabel(f\"(a) Job Count\", fontsize=16)\n",
+    "ax2.set_xlabel(f\"(b) GPU Time\", fontsize=16)\n",
+    "\n",
+    "\n",
+    "###### Fig 3 ######\n",
+    "df_k.sort_values(by=\"count_percent\", ascending=False, inplace=True)\n",
+    "df_k[\"color\"] = [df_s[\"color\"][job_name] for job_name in df_k.index]\n",
+    "\n",
+    "squarify.plot(\n",
+    "    ax=ax3,\n",
+    "    sizes=list(df_k[\"job_count\"].values),\n",
+    "    label=df_k[\"label_percent\"],\n",
+    "    text_kwargs={\"fontsize\": FONT},\n",
+    "    color=df_k[\"color\"],\n",
+    "    bar_kwargs={\"alpha\": 0.8, \"linewidth\": 1, \"edgecolor\": \"k\"},\n",
+    ")\n",
+    "\n",
+    "###### Fig 4 ######\n",
+    "df_k.sort_values(by=\"gtime\", ascending=False, inplace=True)\n",
+    "squarify.plot(\n",
+    "    ax=ax4,\n",
+    "    sizes=list(df_k[\"gtime\"].values),\n",
+    "    label=df_k[\"label_gtime_percent\"],\n",
+    "    text_kwargs={\"fontsize\": FONT},\n",
+    "    color=df_k[\"color\"],\n",
+    "    bar_kwargs={\"alpha\": 0.8, \"linewidth\": 1, \"edgecolor\": \"k\"},\n",
+    ")\n",
+    "\n",
+    "ax1.annotate(\n",
+    "    df_s.at[\"Pretrain\", \"label\"].split(\"\\n\")[1],\n",
+    "    xy=(97, 96),\n",
+    "    xytext=(90, 70),\n",
+    "    arrowprops=dict(facecolor=\"black\", width=2.5, headwidth=8),\n",
+    "    color=\"black\",\n",
+    "    fontsize=15,\n",
+    ")\n",
+    "\n",
+    "ax3.annotate(\n",
+    "    df_k.at[\"Other\", \"label\"].split(\"\\n\")[1],\n",
+    "    xy=(98.5, 92),\n",
+    "    xytext=(80, 80),\n",
+    "    arrowprops=dict(facecolor=\"black\", width=2.5, headwidth=8),\n",
+    "    color=\"black\",\n",
+    "    fontsize=15,\n",
+    ")\n",
+    "\n",
+    "ax4.annotate(\n",
+    "    df_k.at[\"Eval\", \"label_gtime\"].split(\"\\n\")[1],\n",
+    "    xy=(98.5, 93),\n",
+    "    xytext=(80, 80),\n",
+    "    arrowprops=dict(facecolor=\"black\", width=2.5, headwidth=8),\n",
+    "    color=\"black\",\n",
+    "    fontsize=15,\n",
+    ")\n",
+    "\n",
+    "plt.tick_params(axis=\"both\", which=\"both\", bottom=False, top=False, left=False, right=False)\n",
+    "\n",
+    "ax3.set_xlabel(f\"(c) Job Count\", fontsize=16)\n",
+    "ax4.set_xlabel(f\"(d) GPU Time\", fontsize=16, labelpad=8)\n",
+    "\n",
+    "ax1.set_xticks([])\n",
+    "ax1.set_yticks([])\n",
+    "ax2.set_xticks([])\n",
+    "ax2.set_yticks([])\n",
+    "ax3.set_xticks([])\n",
+    "ax3.set_yticks([])\n",
+    "ax4.set_xticks([])\n",
+    "ax4.set_yticks([])\n",
+    "\n",
+    "ax1.text(0.015, 0.03, \"Seren\", transform=ax1.transAxes, size=18, fontweight=\"bold\")\n",
+    "ax2.text(0.02, 0.03, \"Seren\", transform=ax2.transAxes, size=18, fontweight=\"bold\")\n",
+    "ax3.text(0.02, 0.03, \"Kalos\", transform=ax3.transAxes, size=18, fontweight=\"bold\")\n",
+    "ax4.text(0.02, 0.03, \"Kalos\", transform=ax4.transAxes, size=18, fontweight=\"bold\")\n",
+    "fig.savefig(f\"{SAVEPATH}/treemap_job_dist.pdf\", bbox_inches=\"tight\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### CDF: Duration and Queuing Delay of Different Type"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "(a) Seren Duration (b) Seren Queuing  (c) Kalos Duration (d) Kalos Queuing\n",
+    "\"\"\"\n",
+    "\n",
+    "# Duration part\n",
+    "x = [2**i for i in range(0, 22)]\n",
+    "y_gpu_seren_other = calculate_num_cdf_customized_xaxis(\n",
+    "    data_seren[(data_seren[\"gpu_num\"] > 0) & (data_seren[\"type\"] == \"Other\")], x_axis=x, key=\"duration\"\n",
+    ")\n",
+    "y_gpu_seren_debug = calculate_num_cdf_customized_xaxis(\n",
+    "    data_seren[(data_seren[\"gpu_num\"] > 0) & (data_seren[\"type\"] == \"Debug\")], x_axis=x, key=\"duration\"\n",
+    ")\n",
+    "y_gpu_seren_pretrain = calculate_num_cdf_customized_xaxis(\n",
+    "    data_seren[(data_seren[\"gpu_num\"] > 0) & (data_seren[\"type\"] == \"Pretrain\")], x_axis=x, key=\"duration\"\n",
+    ")\n",
+    "y_gpu_seren_eval = calculate_num_cdf_customized_xaxis(\n",
+    "    data_seren[(data_seren[\"gpu_num\"] > 0) & (data_seren[\"type\"] == \"Eval\")], x_axis=x, key=\"duration\"\n",
+    ")\n",
+    "y_gpu_seren_tuning = calculate_num_cdf_customized_xaxis(\n",
+    "    data_seren[(data_seren[\"gpu_num\"] > 0) & (data_seren[\"type\"] == \"SFT\")], x_axis=x, key=\"duration\"\n",
+    ")\n",
+    "y_gpu_seren_mllm = calculate_num_cdf_customized_xaxis(\n",
+    "    data_seren[(data_seren[\"gpu_num\"] > 0) & (data_seren[\"type\"] == \"MLLM\")], x_axis=x, key=\"duration\"\n",
+    ")\n",
+    "\n",
+    "y_gpu_kalos_other = calculate_num_cdf_customized_xaxis(\n",
+    "    data_kalos[(data_kalos[\"gpu_num\"] > 0) & (data_kalos[\"type\"] == \"Other\")], x_axis=x, key=\"duration\"\n",
+    ")\n",
+    "y_gpu_kalos_debug = calculate_num_cdf_customized_xaxis(\n",
+    "    data_kalos[(data_kalos[\"gpu_num\"] > 0) & (data_kalos[\"type\"] == \"Debug\")], x_axis=x, key=\"duration\"\n",
+    ")\n",
+    "y_gpu_kalos_pretrain = calculate_num_cdf_customized_xaxis(\n",
+    "    data_kalos[(data_kalos[\"gpu_num\"] > 0) & (data_kalos[\"type\"] == \"Pretrain\")], x_axis=x, key=\"duration\"\n",
+    ")\n",
+    "y_gpu_kalos_eval = calculate_num_cdf_customized_xaxis(\n",
+    "    data_kalos[(data_kalos[\"gpu_num\"] > 0) & (data_kalos[\"type\"] == \"Eval\")], x_axis=x, key=\"duration\"\n",
+    ")\n",
+    "y_gpu_kalos_tuning = calculate_num_cdf_customized_xaxis(\n",
+    "    data_kalos[(data_kalos[\"gpu_num\"] > 0) & (data_kalos[\"type\"] == \"SFT\")], x_axis=x, key=\"duration\"\n",
+    ")\n",
+    "\n",
+    "# Queuing part\n",
+    "x2 = [2**i for i in range(0, 16)]\n",
+    "y_que_s_other = calculate_num_cdf_customized_xaxis(\n",
+    "    data_seren[(data_seren[\"gpu_num\"] > 0) & (data_seren[\"type\"] == \"Other\")], x_axis=x2, key=\"queue\"\n",
+    ")\n",
+    "y_que_s_debug = calculate_num_cdf_customized_xaxis(\n",
+    "    data_seren[(data_seren[\"gpu_num\"] > 0) & (data_seren[\"type\"] == \"Debug\")], x_axis=x2, key=\"queue\"\n",
+    ")\n",
+    "y_que_s_pretrain = calculate_num_cdf_customized_xaxis(\n",
+    "    data_seren[(data_seren[\"gpu_num\"] > 0) & (data_seren[\"type\"] == \"Pretrain\")], x_axis=x2, key=\"queue\"\n",
+    ")\n",
+    "y_que_s_eval = calculate_num_cdf_customized_xaxis(\n",
+    "    data_seren[(data_seren[\"gpu_num\"] > 0) & (data_seren[\"type\"] == \"Eval\")], x_axis=x2, key=\"queue\"\n",
+    ")\n",
+    "y_que_s_tuning = calculate_num_cdf_customized_xaxis(\n",
+    "    data_seren[(data_seren[\"gpu_num\"] > 0) & (data_seren[\"type\"] == \"SFT\")], x_axis=x2, key=\"queue\"\n",
+    ")\n",
+    "y_que_s_mllm = calculate_num_cdf_customized_xaxis(\n",
+    "    data_seren[(data_seren[\"gpu_num\"] > 0) & (data_seren[\"type\"] == \"MLLM\")], x_axis=x2, key=\"queue\"\n",
+    ")\n",
+    "\n",
+    "y_que_ali_other = calculate_num_cdf_customized_xaxis(\n",
+    "    data_kalos[(data_kalos[\"gpu_num\"] > 0) & (data_kalos[\"type\"] == \"Other\")], x_axis=x2, key=\"queue\"\n",
+    ")\n",
+    "y_que_ali_debug = calculate_num_cdf_customized_xaxis(\n",
+    "    data_kalos[(data_kalos[\"gpu_num\"] > 0) & (data_kalos[\"type\"] == \"Debug\")], x_axis=x2, key=\"queue\"\n",
+    ")\n",
+    "y_que_ali_pretrain = calculate_num_cdf_customized_xaxis(\n",
+    "    data_kalos[(data_kalos[\"gpu_num\"] > 0) & (data_kalos[\"type\"] == \"Pretrain\")], x_axis=x2, key=\"queue\"\n",
+    ")\n",
+    "y_que_ali_eval = calculate_num_cdf_customized_xaxis(\n",
+    "    data_kalos[(data_kalos[\"gpu_num\"] > 0) & (data_kalos[\"type\"] == \"Eval\")], x_axis=x2, key=\"queue\"\n",
+    ")\n",
+    "y_que_ali_tuning = calculate_num_cdf_customized_xaxis(\n",
+    "    data_kalos[(data_kalos[\"gpu_num\"] > 0) & (data_kalos[\"type\"] == \"SFT\")], x_axis=x2, key=\"queue\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "linestyles = [\"--\", \"-.\", \":\", \"--\", \"-.\", \":\"]\n",
+    "grid_params = dict(width_ratios=[1, 1])\n",
+    "fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(ncols=2, nrows=2, constrained_layout=True, figsize=(9, 7))\n",
+    "\n",
+    "# (a) Seren Duration\n",
+    "ax1.plot(x, y_gpu_seren_eval, linestyles[0], linewidth=3, alpha=0.9, color=cmp[0], label=\"Evaluation\")\n",
+    "ax1.plot(x, y_gpu_seren_pretrain, linestyles[1], linewidth=3, alpha=0.9, color=cmp[1], label=\"Pretrain\")\n",
+    "ax1.plot(x, y_gpu_seren_tuning, linestyles[2], linewidth=3, alpha=0.9, color=cmp[2], label=\"SFT\")\n",
+    "ax1.plot(x, y_gpu_seren_mllm, linestyles[0], linewidth=3, alpha=0.9, color=cmp[3], label=\"MLLM\")\n",
+    "ax1.plot(x, y_gpu_seren_debug, linestyles[1], linewidth=3, alpha=0.9, color=cmp[4], label=\"Debug\")\n",
+    "ax1.plot(x, y_gpu_seren_other, linestyles[2], linewidth=3, alpha=0.9, color=cmp[5], label=\"Other\")\n",
+    "\n",
+    "\n",
+    "# (b) Seren Queuing\n",
+    "ax2.plot(x2, y_que_s_eval, linestyles[0], linewidth=3, alpha=0.9, color=cmp[0], label=\"Evaluation\")\n",
+    "ax2.plot(x2, y_que_s_pretrain, linestyles[1], linewidth=3, alpha=0.9, color=cmp[1], label=\"Pretrain\")\n",
+    "ax2.plot(x2, y_que_s_tuning, linestyles[2], linewidth=3, alpha=0.9, color=cmp[2], label=\"SFT\")\n",
+    "ax2.plot(x2, y_que_s_mllm, linestyles[0], linewidth=3, alpha=0.9, color=cmp[3], label=\"MLLM\")\n",
+    "ax2.plot(x2, y_que_s_debug, linestyles[1], linewidth=3, alpha=0.9, color=cmp[4], label=\"Debug\")\n",
+    "ax2.plot(x2, y_que_s_other, linestyles[2], linewidth=3, alpha=0.9, color=cmp[5], label=\"Other\")\n",
+    "\n",
+    "# (c) Kalos Duration\n",
+    "ax3.plot(x, y_gpu_kalos_eval, linestyles[0], linewidth=3, alpha=0.9, color=cmp[0], label=\"Evaluation\")\n",
+    "ax3.plot(x, y_gpu_kalos_pretrain, linestyles[1], linewidth=3, alpha=0.9, color=cmp[1], label=\"Pretrain\")\n",
+    "ax3.plot(x, y_gpu_kalos_debug, linestyles[1], linewidth=3, alpha=0.9, color=cmp[4], label=\"Debug\")\n",
+    "ax3.plot(x, y_gpu_kalos_other, linestyles[2], linewidth=3, alpha=0.9, color=cmp[5], label=\"Other\")\n",
+    "\n",
+    "\n",
+    "# (d) Kalos Queuing\n",
+    "ax4.plot(x2, y_que_ali_eval, linestyles[0], linewidth=3, alpha=0.9, color=cmp[0], label=\"Evaluation\")\n",
+    "ax4.plot(x2, y_que_ali_pretrain, linestyles[1], linewidth=3, alpha=0.9, color=cmp[1], label=\"Pretrain\")\n",
+    "ax4.plot(x2, y_que_ali_debug, linestyles[1], linewidth=3, alpha=0.9, color=cmp[4], label=\"Debug\")\n",
+    "ax4.plot(x2, y_que_ali_other, linestyles[2], linewidth=3, alpha=0.9, color=cmp[5], label=\"Other\")\n",
+    "\n",
+    "ax1.set_xlabel(f\"(a) Job Duration (s)\")\n",
+    "ax1.set_ylabel(f\"CDF (%)\")\n",
+    "ax1.set_xscale(\"log\")\n",
+    "ax1.set_xticks([1e0, 1e1, 1e2, 1e3, 1e4, 1e5, 1e6])\n",
+    "ax1.set_xlim(1, x[-1])\n",
+    "ax1.set_ylim(-0.5, 100.8)\n",
+    "handles, labels = ax1.get_legend_handles_labels()\n",
+    "fig.legend(handles=handles, labels=labels, ncols=6, bbox_to_anchor=(-0.01, 1.08), loc=2, columnspacing=0.9, handletextpad=0.2)\n",
+    "ax1.grid(linestyle=\":\")\n",
+    "\n",
+    "ax2.set_xlabel(f\"(b) Job Queuing Delay (s)\")\n",
+    "ax2.set_ylabel(f\"CDF (%)\")\n",
+    "ax2.set_xscale(\"log\")\n",
+    "ax2.set_xticks([1e0, 1e1, 1e2, 1e3, 1e4])\n",
+    "ax2.set_xlim(1, x2[-1])\n",
+    "ax2.set_ylim(-0.5, 100.8)\n",
+    "ax2.grid(linestyle=\":\")\n",
+    "\n",
+    "ax3.set_xlabel(f\"(c) Job Duration (s)\")\n",
+    "ax3.set_ylabel(f\"CDF (%)\")\n",
+    "ax3.set_xscale(\"log\")\n",
+    "ax3.set_xticks([1e0, 1e1, 1e2, 1e3, 1e4, 1e5, 1e6])\n",
+    "ax3.set_xlim(1, x[-1])\n",
+    "ax3.set_ylim(-0.5, 100.8)\n",
+    "ax3.grid(linestyle=\":\")\n",
+    "\n",
+    "ax4.set_xlabel(f\"(d) Job Queuing Delay (s)\")\n",
+    "ax4.set_ylabel(f\"CDF (%)\")\n",
+    "ax4.set_xscale(\"log\")\n",
+    "ax4.set_xticks([1e0, 1e1, 1e2, 1e3, 1e4])\n",
+    "ax4.set_xlim(1, x2[-1])\n",
+    "ax4.set_ylim(-0.5, 100.8)\n",
+    "ax4.grid(linestyle=\":\")\n",
+    "\n",
+    "# 1 hour and 1 day\n",
+    "ax1.axvline(x=3600, ls=\"--\", alpha=0.6, c=\"gray\", ymax=0.94, lw=1.5)\n",
+    "ax1.axvline(x=3600 * 24, ls=\"--\", alpha=0.9, c=\"gray\", ymax=0.94, lw=1.5)\n",
+    "ax3.axvline(x=3600, ls=\"--\", alpha=0.6, c=\"gray\", ymax=0.94, lw=1.5)\n",
+    "ax3.axvline(x=3600 * 24, ls=\"--\", alpha=0.9, c=\"gray\", ymax=0.94, lw=1.5)\n",
+    "\n",
+    "sns.despine()\n",
+    "ax1.text(0.78, 0.03, \"Seren\", transform=ax1.transAxes, size=20, fontweight=\"bold\")\n",
+    "ax2.text(0.78, 0.03, \"Seren\", transform=ax2.transAxes, size=20, fontweight=\"bold\")\n",
+    "ax3.text(0.78, 0.03, \"Kalos\", transform=ax3.transAxes, size=20, fontweight=\"bold\")\n",
+    "ax4.text(0.78, 0.03, \"Kalos\", transform=ax4.transAxes, size=20, fontweight=\"bold\")\n",
+    "\n",
+    "fig.savefig(f\"{SAVEPATH}/cdf_job_duration_queue.pdf\", bbox_inches=\"tight\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Box Plot: Request GPU number Different Type"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cmap = sns.color_palette(\"pastel\")\n",
+    "fig, (ax1, ax2) = plt.subplots(\n",
+    "    ncols=2,\n",
+    "    nrows=1,\n",
+    "    gridspec_kw={\"width_ratios\": [4.2, 3]},\n",
+    "    constrained_layout=True,\n",
+    "    figsize=(9, 3.75),\n",
+    ")\n",
+    "\n",
+    "############ Fig 1 ############\n",
+    "data_seren.sort_values(by=\"gpu_num\", ascending=False, inplace=True)\n",
+    "data_seren[\"type\"].replace(\"SFT\", \"SFT\", inplace=True)\n",
+    "\n",
+    "x_ticks = [\n",
+    "    \"Eval\",\n",
+    "    \"Pretrain\",\n",
+    "    \"SFT\",\n",
+    "    \"MLLM\",\n",
+    "    \"Debug\",\n",
+    "    \"Other\",\n",
+    "]\n",
+    "\n",
+    "flierprops = dict(marker=\".\", markerfacecolor=\"k\", markersize=2, linestyle=\"none\")\n",
+    "sns.boxplot(\n",
+    "    x=\"type\",\n",
+    "    y=\"gpu_num\",\n",
+    "    data=data_seren,\n",
+    "    flierprops=flierprops,\n",
+    "    width=0.6,\n",
+    "    linewidth=2.2,\n",
+    "    saturation=2,\n",
+    "    palette=cmap,\n",
+    "    ax=ax1,\n",
+    "    order=x_ticks,\n",
+    "    boxprops=dict(alpha=1),\n",
+    ")\n",
+    "sns.color_palette(\"tab10\")\n",
+    "ax1.set_xlabel(\"(a) Seren\")\n",
+    "ax1.set_xticklabels(ax1.get_xticklabels(), rotation=0)\n",
+    "ax1.set_ylabel(f\"Number of GPUs\")\n",
+    "ax1.set_yscale(\"log\")\n",
+    "ax1.grid(axis=\"y\", linestyle=\":\")\n",
+    "\n",
+    "\n",
+    "############ Fig 2 ############\n",
+    "data_kalos.sort_values(by=\"gpu_num\", ascending=False, inplace=True)\n",
+    "data_kalos = data_kalos[data_kalos[\"type\"] != \"SFT\"]\n",
+    "x_ticks_k = [\n",
+    "    \"Eval\",\n",
+    "    \"Pretrain\",\n",
+    "    \"Debug\",\n",
+    "    \"Other\",\n",
+    "]\n",
+    "my_pal = [cmap[0], cmap[1], cmap[4], cmap[5]]\n",
+    "\n",
+    "flierprops = dict(marker=\".\", markerfacecolor=\"k\", markersize=3, linestyle=\"none\")\n",
+    "sns.boxplot(\n",
+    "    x=\"type\",\n",
+    "    y=\"gpu_num\",\n",
+    "    data=data_kalos,\n",
+    "    flierprops=flierprops,\n",
+    "    width=0.6,\n",
+    "    linewidth=2.2,\n",
+    "    saturation=2,\n",
+    "    palette=my_pal,\n",
+    "    ax=ax2,\n",
+    "    order=x_ticks_k,\n",
+    "    boxprops=dict(alpha=1),\n",
+    ")\n",
+    "sns.color_palette(\"tab10\")\n",
+    "ax2.set_xlabel(\"(b) Kalos\")\n",
+    "ax2.set_ylabel(None)\n",
+    "ax2.set_xticklabels(ax2.get_xticklabels(), rotation=0)\n",
+    "ax2.set_yscale(\"log\")\n",
+    "ax2.grid(axis=\"y\", linestyle=\":\")\n",
+    "\n",
+    "sns.despine()\n",
+    "fig.savefig(f\"{SAVEPATH}/box_gpu_num.pdf\", bbox_inches=\"tight\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### CDF: Resource Utilization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(f\"{PKLPATH}/util_gpu_seren.pkl\", \"rb\") as file:\n",
+    "    _, _, x2, y2, x3, y3, x4, y4, x5, y5 = pickle.load(file)\n",
+    "with open(f\"{PKLPATH}/util_gpu_kalos_full.pkl\", \"rb\") as file:\n",
+    "    _, _, x2_k, y2_k, x3_k, y3_k, x4_k, y4_k, x5_k, y5_k = pickle.load(file)\n",
+    "with open(f\"{PKLPATH}/util_cpu_mem_seren.pkl\", \"rb\") as file:\n",
+    "    x6, y6, x7, y7 = pickle.load(file)\n",
+    "with open(f\"{PKLPATH}/util_cpu_mem_kalos.pkl\", \"rb\") as file:\n",
+    "    x6_k, y6_k, x7_k, y7_k = pickle.load(file)\n",
+    "with open(f\"{PKLPATH}/ib_seren.pkl\", \"rb\") as file:\n",
+    "    x8, y8, x9, y9 = pickle.load(file)\n",
+    "\n",
+    "x8 = x8 / x8.max() * 100\n",
+    "x9 = x9 / x9.max() * 100\n",
+    "\n",
+    "linestyles = [\"--\", \":\", \"--\", \"-.\", \":\"]\n",
+    "grid_params = dict(width_ratios=[1, 1])\n",
+    "fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(ncols=2, nrows=2, constrained_layout=True, figsize=(9, 7))\n",
+    "\n",
+    "############ Fig 1: SM, Occupancy ############\n",
+    "ax1.plot(x3, y3, linestyles[0], linewidth=3, alpha=0.9, color=cmp[0], label=\"Seren SM Activity\")\n",
+    "ax1.plot(x5, y5, linestyles[1], linewidth=3, alpha=0.9, color=cmp[0], label=\"Seren Occupancy\")\n",
+    "ax1.plot(x3_k, y3_k, linestyles[0], linewidth=3, alpha=0.9, color=cmp[1], label=\"Kalos SM Activity\")\n",
+    "ax1.plot(x5_k, y5_k, linestyles[1], linewidth=3, alpha=0.9, color=cmp[1], label=\"Kalos Occupancy\")\n",
+    "\n",
+    "############ Fig 2: CPU mem usage, GPU mem usage ############\n",
+    "ax2.plot(x7, y7, linestyles[0], linewidth=3, alpha=0.9, color=cmp[0], label=\"Seren CPU Mem\")\n",
+    "ax2.plot(x2, y2, linestyles[1], linewidth=3, alpha=0.9, color=cmp[0], label=\"Seren GPU Mem\")\n",
+    "ax2.plot(x7_k, y7_k, linestyles[0], linewidth=3, alpha=0.9, color=cmp[1], label=\"Kalos CPU Mem\")\n",
+    "ax2.plot(x2_k, y2_k, linestyles[1], linewidth=3, alpha=0.9, color=cmp[1], label=\"Kalos GPU Mem\")\n",
+    "\n",
+    "############ Fig 3: CPU util ############\n",
+    "ax3.plot(x6, y6, linestyles[0], linewidth=3, alpha=0.9, color=cmp[0], label=\"Seren\")\n",
+    "ax3.plot(x6_k, y6_k, linestyles[0], linewidth=3, alpha=0.9, color=cmp[1], label=\"Kalos\")\n",
+    "\n",
+    "############ Fig 4: IB send, receive ############\n",
+    "ax4.plot(x8, y8, linestyles[0], linewidth=3, alpha=0.9, color=cmp[0], label=\"IB Send\")\n",
+    "ax4.plot(x9, y9, linestyles[1], linewidth=3, alpha=0.9, color=cmp[0], label=\"IB Receive\")\n",
+    "\n",
+    "ax1.set_xlabel(f\"(a) GPU DCGM Metric (%)\")\n",
+    "ax1.set_ylabel(f\"CDF (%)\")\n",
+    "ax1.set_xlim(-0.8, 100.8)\n",
+    "ax1.set_ylim(0, 100.8)\n",
+    "ax1.set_xticks([0, 25, 50, 75, 100])\n",
+    "ax1.grid(linestyle=\":\")\n",
+    "\n",
+    "ax2.set_xlabel(f\"(b) Memory Footprint (%)\")\n",
+    "ax2.set_ylabel(f\"CDF (%)\")\n",
+    "ax2.set_xlim(-0.8, 100.8)\n",
+    "ax2.set_xticks([0, 25, 50, 75, 100])\n",
+    "ax2.set_ylim(0, 100.8)\n",
+    "ax2.grid(linestyle=\":\")\n",
+    "\n",
+    "ax3.set_xlabel(f\"(c) CPU Utilization (%)\")\n",
+    "ax3.set_ylabel(f\"CDF (%)\")\n",
+    "ax3.set_xlim(-0.8, 100.8)\n",
+    "ax3.set_xticks([0, 25, 50, 75, 100])\n",
+    "ax3.set_ylim(0, 100.8)\n",
+    "ax3.legend(loc=\"lower right\")\n",
+    "ax3.grid(linestyle=\":\")\n",
+    "\n",
+    "ax4.set_xlabel(f\"(d) Network (%)\")\n",
+    "ax4.set_ylabel(f\"CDF (%)\")\n",
+    "ax4.set_xlim(-0.8, 100.8)\n",
+    "ax4.set_xticks([0, 25, 50, 75, 100])\n",
+    "ax4.set_ylim(0, 100.8)\n",
+    "ax4.legend(loc=\"lower right\")\n",
+    "ax4.grid(linestyle=\":\")\n",
+    "sns.despine()\n",
+    "\n",
+    "\n",
+    "S = mpatches.Patch(facecolor=cmp[0], alpha=0.9)\n",
+    "K = mpatches.Patch(facecolor=cmp[1], alpha=0.9)\n",
+    "A = (Line2D([0], [0], color=\"black\", lw=3, ls=\"--\"),)\n",
+    "B = (Line2D([0], [0], color=\"black\", lw=3, ls=\":\"),)\n",
+    "\n",
+    "legend1 = ax1.legend([S, K], [\"Seren\", \"Kalos\"], bbox_to_anchor=(0.5, 0.62), loc=2, ncol=1, fontsize=17, frameon=False)\n",
+    "\n",
+    "ax1.add_artist(legend1)\n",
+    "\n",
+    "ax1.legend([A, B], [\"SM Activity\", \"TC Activity\"], bbox_to_anchor=(0.3, 0.36), loc=2, ncol=1)\n",
+    "\n",
+    "ax2.legend([A, B], [\"CPU Memory\", \"GPU Memory\"], bbox_to_anchor=(0.25, 0.36), loc=2, ncol=1)\n",
+    "\n",
+    "fig.savefig(f\"{SAVEPATH}/cdf_resource_util.pdf\", bbox_inches=\"tight\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### CDF: Temperature"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# We use August data for GPU temperature and power\n",
+    "with open(f\"{PKLPATH}/gpu_temp_seren.pkl\", \"rb\") as file:\n",
+    "    x, y1, x2, y2 = pickle.load(file)\n",
+    "with open(f\"{PKLPATH}/gpu_temp_kalos.pkl\", \"rb\") as file:\n",
+    "    x1_k, y1_k, x2_k, y2_k = pickle.load(file)\n",
+    "with open(f\"{PKLPATH}/gpu_power_seren.pkl\", \"rb\") as file:\n",
+    "    x3, y3 = pickle.load(file)\n",
+    "with open(f\"{PKLPATH}/gpu_power_kalos.pkl\", \"rb\") as file:\n",
+    "    x3_k, y3_k = pickle.load(file)\n",
+    "\n",
+    "linestyles = [\"-\", \":\", \":\", \"-\"]\n",
+    "fig, ax1 = plt.subplots(ncols=1, nrows=1, constrained_layout=True, figsize=(5, 3.75))\n",
+    "\n",
+    "############ Fig 1: Temperature ############\n",
+    "ax1.plot(x, y1, linestyles[0], linewidth=3, alpha=0.9, color=cmp[0], label=\"Seren GPU Temp\")\n",
+    "ax1.plot(x2, y2, linestyles[1], linewidth=3, alpha=0.9, color=cmp[0], label=\"Seren GPU Mem Temp\")\n",
+    "ax1.plot(x1_k, y1_k, linestyles[0], linewidth=3, alpha=0.9, color=cmp[1], label=\"Kalos GPU Temp\")\n",
+    "ax1.plot(x2_k, y2_k, linestyles[1], linewidth=3, alpha=0.9, color=cmp[1], label=\"Kalos GPU Mem Temp\")\n",
+    "\n",
+    "ax1.set_xlabel(f\"Temperature (°C)\")\n",
+    "ax1.set_ylabel(f\"CDF (%)\")\n",
+    "ax1.set_xlim(20, 85)\n",
+    "ax1.set_ylim(0, 100.8)\n",
+    "ax1.grid(linestyle=\":\")\n",
+    "\n",
+    "S = mpatches.Patch(facecolor=cmp[0], alpha=0.9)\n",
+    "K = mpatches.Patch(facecolor=cmp[1], alpha=0.9)\n",
+    "A = (Line2D([0], [0], color=\"black\", lw=3, ls=\"-\"),)\n",
+    "B = (Line2D([0], [0], color=\"black\", lw=3, ls=\":\"),)\n",
+    "\n",
+    "legend1 = ax1.legend([S, K], [\"Seren\", \"Kalos\"], bbox_to_anchor=(0.6, 0.62), loc=2, ncol=1, fontsize=17, frameon=False)\n",
+    "\n",
+    "ax1.add_artist(legend1)\n",
+    "\n",
+    "ax1.legend(\n",
+    "    [A, B],\n",
+    "    [\"GPU Temp.\", \"GMem Temp.\"],\n",
+    "    bbox_to_anchor=(0.4, 0.32),\n",
+    "    loc=2,\n",
+    "    ncol=1,\n",
+    "    fontsize=17,\n",
+    ")\n",
+    "\n",
+    "sns.despine()\n",
+    "fig.savefig(f\"{SAVEPATH}/cdf_temperature.pdf\", bbox_inches=\"tight\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### CDF: Power"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(f\"{PKLPATH}/server_power.pkl\", \"rb\") as file:\n",
+    "    x1, y1, x2, y2 = pickle.load(file)\n",
+    "with open(f\"{PKLPATH}/gpu_power_seren.pkl\", \"rb\") as file:\n",
+    "    x3, y3 = pickle.load(file)\n",
+    "with open(f\"{PKLPATH}/gpu_power_kalos.pkl\", \"rb\") as file:\n",
+    "    x3_k, y3_k = pickle.load(file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "linestyles = [\"--\", \":\", \":\", \"-\"]\n",
+    "grid_params = dict(width_ratios=[1, 1])\n",
+    "fig, (ax1, ax2) = plt.subplots(ncols=2, nrows=1, constrained_layout=True, figsize=(9, 3.75))\n",
+    "\n",
+    "############ Fig 1: GPU power ############\n",
+    "ax1.plot(x3, y3, linestyles[0], linewidth=3, alpha=0.9, color=cmp[0], label=\"Seren\")\n",
+    "ax1.plot(x3_k, y3_k, linestyles[0], linewidth=3, alpha=0.9, color=cmp[1], label=\"Kalos\")\n",
+    "ax1.axvline(x=400, ls=\"--\", alpha=0.6, c=\"gray\", ymax=100, lw=1.5)\n",
+    "ax1.annotate(\n",
+    "    \"A100 TDP\",\n",
+    "    xy=(400, 33),\n",
+    "    xytext=(420, 20),\n",
+    "    arrowprops=dict(facecolor=\"black\", width=2.5, headwidth=8),\n",
+    "    color=\"black\",\n",
+    "    fontsize=16,\n",
+    ")\n",
+    "ax1.annotate(\n",
+    "    \"Max=600\",\n",
+    "    xy=(600, 100),\n",
+    "    xytext=(430, 85),\n",
+    "    arrowprops=dict(facecolor=\"black\", width=2.5, headwidth=8),\n",
+    "    color=\"black\",\n",
+    "    fontsize=16,\n",
+    ")\n",
+    "\n",
+    "############ Fig 2: Server power ############\n",
+    "ax2.plot(x1, y1, linestyles[0], linewidth=3, alpha=0.9, color=cmp[0], label=\"GPU Node\")\n",
+    "ax2.plot(x2, y2, linestyles[1], linewidth=3, alpha=0.9, color=cmp[0], label=\"CPU Node\")\n",
+    "ax2.annotate(\n",
+    "    \"Max=960\",\n",
+    "    xy=(960, 100),\n",
+    "    xytext=(1200, 90),\n",
+    "    arrowprops=dict(facecolor=\"black\", width=2.5, headwidth=8),\n",
+    "    color=\"black\",\n",
+    "    fontsize=16,\n",
+    ")\n",
+    "ax2.annotate(\n",
+    "    \"Max=6550\",\n",
+    "    xy=(6550, 100),\n",
+    "    xytext=(4500, 70),\n",
+    "    arrowprops=dict(facecolor=\"black\", width=2.5, headwidth=8),\n",
+    "    color=\"black\",\n",
+    "    fontsize=16,\n",
+    ")\n",
+    "\n",
+    "ax1.set_xlabel(f\"(a) GPU Power (W)\")\n",
+    "ax1.set_ylabel(f\"CDF (%)\")\n",
+    "ax1.set_xlim(-0.8, 610)\n",
+    "ax1.set_ylim(0, 100.8)\n",
+    "ax1.legend()\n",
+    "ax1.grid(linestyle=\":\")\n",
+    "ax1.xaxis.set_minor_locator(matplotlib.ticker.FixedLocator([60]))\n",
+    "ax1.xaxis.set_minor_formatter(matplotlib.ticker.FixedFormatter([60]))\n",
+    "ax1.tick_params(axis=\"x\", which=\"minor\", labelsize=15)\n",
+    "\n",
+    "ax2.set_xlabel(f\"(b) Server Power in Seren (W)\")\n",
+    "ax2.set_ylabel(f\"CDF (%)\")\n",
+    "ax2.set_xlim(-0.8, x1.max())\n",
+    "ax2.set_ylim(0, 100.8)\n",
+    "ax2.legend(loc=\"lower right\")\n",
+    "ax2.grid(linestyle=\":\")\n",
+    "ax2.xaxis.set_minor_locator(matplotlib.ticker.FixedLocator([520]))\n",
+    "ax2.xaxis.set_minor_formatter(matplotlib.ticker.FixedFormatter([520]))\n",
+    "ax2.tick_params(axis=\"x\", which=\"minor\", labelsize=15)\n",
+    "sns.despine()\n",
+    "\n",
+    "fig.savefig(f\"{SAVEPATH}/cdf_power.pdf\", bbox_inches=\"tight\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.16"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

data/cluster_summary.csv

@@ -0,0 +1,6 @@
+id,job_num,cpu_job_num,gpu_job_num,avg_run_time_gpu,avg_que_time_gpu,avg_gpu_num,med_run_time_gpu,med_que_time_gpu,med_gpu_num,max_run_time_gpu,max_gpu,complete_rate_gpu,cancel_rate_gpu,fail_rate_gpu,complete_gpu_time,cancel_gpu_time,fail_gpu_time,complete_rate_gpu_time,cancel_rate_gpu_time,fail_rate_gpu_time,avg_run_time_cpu,avg_que_time_cpu,med_run_time_cpu,med_que_time_cpu,complete_rate_cpu,cancel_rate_cpu,fail_rate_cpu
+Seren,1031550,367737,663813,1414.335,445.984,5.68,122.0,1.0,1.0,1209604.0,1024.0,0.497,0.075,0.429,4560592618.0,14274584510.0,2675447459.0,0.212,0.664,0.124,733.894,4.813,14.0,1.0,0.858,0.039,0.102
+Kalos,62413,42506,19907,1259.689,214.179,26.77,124.0,17.0,1.0,625740.0,1024.0,0.541,0.062,0.397,1667685146.0,3239202064.0,426292851.0,0.313,0.607,0.08,118.352,88.401,22.0,9.0,0.86,0.001,0.14
+Philly,112956,0,112956,18006.055,3760.604,1.928,874.0,189.0,1.0,4240676.0,128.0,0.604,0.09,0.305,2210176414.0,2025326056.0,2659215061.0,0.321,0.294,0.386,,,,,,,
+Helios,3362981,1782517,1580464,6651.681,862.047,3.716,206.0,0.0,1.0,4320009.0,2048.0,0.624,0.221,0.155,39994296320.0,30715109125.0,7288334702.0,0.513,0.394,0.093,628.759,73.907,2.0,0.0,0.909,0.03,0.061
+PAI,1260920,0,1037085,4787.109,401.742,0.683,480.667,9.0,0.5,626371.0,8.0,,,,,,,,,,,,,,,,

data/generate_utilization_pkl.ipynb

@@ -0,0 +1,292 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Process Utilization Files and Generate a Pickle Files for Plotting"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Note that running following scripts is time consuming and should be done only once. The pickle files are provided in the `util_pkl` directory."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pytz\n",
+    "import pickle\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "\n",
+    "from glob import glob\n",
+    "from pathlib import Path\n",
+    "from utils import cluster_metric_header, dcgm_metric_header\n",
+    "\n",
+    "\"\"\" cluster_metric_header\n",
+    "0: \"CPU\",\n",
+    "1: \"MEMORY\",\n",
+    "2: \"IB_SEND\",\n",
+    "3: \"RECEIVE\",\n",
+    "\"\"\"\n",
+    "\"\"\" dcgm_metric_header\n",
+    "0: XID_ERRORS\n",
+    "1: GPU_TEMP\n",
+    "2: MEMORY_TEMP\n",
+    "3: MEM_CLOCK\n",
+    "4: MEM_COPY_UTIL\n",
+    "5: FB_FREE\n",
+    "6: FB_USED\n",
+    "7: DRAM_ACTIVE\n",
+    "8: POWER_USAGE\n",
+    "9: GPU_UTIL\n",
+    "10: PIPE_TENSOR_ACTIVE\n",
+    "11: SM_ACTIVE\n",
+    "12: SM_OCCUPANCY\n",
+    "\"\"\"\n",
+    "\n",
+    "SAVEPKL = \"./util_pkl\"\n",
+    "\n",
+    "\n",
+    "def read_csv_with_concat(path=\"./csv\", file_name=None):\n",
+    "    file = Path(path, f\"{file_name}.csv\")\n",
+    "\n",
+    "    if file.exists():\n",
+    "        # If original file exists, read it directly\n",
+    "        df = pd.read_csv(file)\n",
+    "        print(f\"Reading {file_name}\")\n",
+    "    else:\n",
+    "        # If original file does not exist, read all the split files\n",
+    "        split_files = sorted(glob(f\"{path}/{file_name}-2023-*.csv\"))\n",
+    "        print(f\"Reading splitted files: {split_files}\")\n",
+    "        df = pd.concat([pd.read_csv(split_file) for split_file in split_files])\n",
+    "        df.reset_index(drop=True, inplace=True)\n",
+    "    return df\n",
+    "\n",
+    "\n",
+    "def read_concat_parse_save_cluster_metrics(path=\"./csv_cpu\", metrics=cluster_metric_header):\n",
+    "    for metric in metrics:\n",
+    "        data = read_csv_with_concat(path=path, file_name=metric)\n",
+    "        data.drop_duplicates(subset=[\"Time\"], inplace=True)\n",
+    "        data.sort_values(by=\"Time\", inplace=True)\n",
+    "        data[\"Time\"] = pd.to_datetime(data[\"Time\"], unit=\"s\").dt.tz_localize(pytz.utc).dt.tz_convert(\"Asia/Shanghai\")\n",
+    "        data.set_index(\"Time\", drop=True, inplace=True)\n",
+    "        print(f\"Column Number: {len(list(data.columns))}, {len(set(list(data.columns)))}\")\n",
+    "\n",
+    "        if \"NODE_MEMORY\" in metric:\n",
+    "            # Around 2 hours has some bug (ip has additional '.1', like '10.140.0.131' -> '10.140.0.131.1')\n",
+    "            data = data[(data.index < \"2023-07-19 11:35:00\") | (data.index > \"2023-07-19 14:01:00\")]\n",
+    "\n",
+    "        if \"NODE_CPU\" in metric or \"NODE_MEMORY\" in metric:\n",
+    "            data = data * 100  # CPU / Memory Utilization (%)\n",
+    "\n",
+    "        if \"NODE_IB\" in metric:\n",
+    "            data.rename(columns=lambda x: x.replace(\"-mlx5_0\", \"\"), inplace=True)  # Simplified, since one IB NIC per server\n",
+    "\n",
+    "        data.dropna(axis=1, how=\"all\", inplace=True)\n",
+    "        data = data.round(3)\n",
+    "        data.to_csv(f\"./{metric}.csv\")\n",
+    "\n",
+    "\n",
+    "def read_concat_parse_save_dcgm_metrics(path=\"./csv\", metrics=dcgm_metric_header):\n",
+    "    for metric in metrics:\n",
+    "        data = read_csv_with_concat(path=path, file_name=metric)\n",
+    "        data.drop_duplicates(subset=[\"Time\"], inplace=True)\n",
+    "        data.sort_values(by=\"Time\", inplace=True)\n",
+    "        data[\"Time\"] = pd.to_datetime(data[\"Time\"], unit=\"s\").dt.tz_localize(pytz.utc).dt.tz_convert(\"Asia/Shanghai\")\n",
+    "        data.set_index(\"Time\", drop=True, inplace=True)\n",
+    "        print(f\"Column Number: {len(list(data.columns))}, {len(set(list(data.columns)))}\")\n",
+    "\n",
+    "        # if \"XID\" in metric or \"TEMP\" in metric or \"CLOC\" in metric:\n",
+    "        #     data = data.astype(int, errors='ignore')\n",
+    "\n",
+    "        if \"ACTIVE\" in metric or \"OCCUPANCY\" in metric:\n",
+    "            data = data * 100  # CPU / Memory Utilization (%)\n",
+    "            data = data.round(3)\n",
+    "\n",
+    "        if \"POWER\" in metric:\n",
+    "            data = data.round(1)\n",
+    "\n",
+    "        data.dropna(axis=0, how=\"all\", inplace=True)\n",
+    "        data.dropna(axis=1, how=\"all\", inplace=True)\n",
+    "        data.to_csv(f\"./{metric}.csv\")\n",
+    "\n",
+    "\n",
+    "def calculate_sum_cdf_axis100(df, dot_num=1000):\n",
+    "    \"\"\"\n",
+    "    Calculate quantity percentile CDF, y-axis: 0-100%,\n",
+    "    \"\"\"\n",
+    "    print(\"Parsing\")\n",
+    "    data = df.melt(id_vars=\"Time\", var_name=\"Server\")\n",
+    "    data.dropna(subset=[\"value\"], inplace=True)\n",
+    "\n",
+    "    y = np.linspace(0, 1, num=dot_num)\n",
+    "    x = data[\"value\"].quantile(y).values\n",
+    "    y = y * 100\n",
+    "    return x, y\n",
+    "\n",
+    "\n",
+    "def calculate_num_cdf_axis100(df, dot_num=1000):\n",
+    "    \"\"\"\n",
+    "    Calculate quantity percentile CDF, y-axis: 0-100%,\n",
+    "    \"\"\"\n",
+    "    print(\"Parsing\")\n",
+    "    data = df.melt(id_vars=\"Time\", var_name=\"Server\")\n",
+    "    data.dropna(subset=[\"value\"], inplace=True)\n",
+    "    # data.sort_values('value', ascending=True, inplace=True)\n",
+    "    # data.reset_index(drop=True, inplace=True)\n",
+    "\n",
+    "    y = np.linspace(0, 1, num=dot_num)\n",
+    "    x = data[\"value\"].quantile(y).values\n",
+    "    y = y * 100\n",
+    "    return x, y"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Example 1: Prometheus Metics (e.g., CPU and Memory Utilization)\n",
+    "\n",
+    "You can change to any metric you want to plot by changing the `file_name` variable in the following script."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_cpu = read_csv_with_concat(path=\"./seren\", file_name=cluster_metric_header[0])\n",
+    "data_mem = read_csv_with_concat(path=\"./seren\", file_name=cluster_metric_header[1])\n",
+    "x1, y1 = calculate_num_cdf_axis100(data_cpu)\n",
+    "x2, y2 = calculate_num_cdf_axis100(data_mem)\n",
+    "print(\n",
+    "    f'CPU Period: (Start) {data_cpu.at[0, \"Time\"].split(\":\")[0]}h (End) {data_cpu.at[len(data_cpu)-1, \"Time\"].split(\":\")[0]}h'\n",
+    ")\n",
+    "print(\n",
+    "    f'MEM Period: (Start) {data_mem.at[0, \"Time\"].split(\":\")[0]}h (End) {data_mem.at[len(data_mem)-1, \"Time\"].split(\":\")[0]}h'\n",
+    ")\n",
+    "\n",
+    "with open(f\"{SAVEPKL}/util_cpu_mem_seren.pkl\", \"wb\") as file:\n",
+    "    pickle.dump([x1, y1, x2, y2], file)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Example 2: NVIDIA DCGM Metics (e.g., GPU and GPU Memory Utilization)\n",
+    "\n",
+    "You can change to any metric you want to plot by changing the `file_name` variable in the following script."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_gpu_util = read_csv_with_concat(path=\"./seren\", file_name=dcgm_metric_header[9])  # \"DCGM_FI_DEV_GPU_UTIL\"\n",
+    "data_gpu_mem = read_csv_with_concat(path=\"./seren\", file_name=dcgm_metric_header[6])  # \"DCGM_FI_DEV_FB_USED\"\n",
+    "data_gpu_mem.iloc[:, 1:] = 100 * data_gpu_mem.iloc[:, 1:] / (80 * 1024)\n",
+    "x1, y1 = calculate_num_cdf_axis100(data_gpu_util)\n",
+    "x2, y2 = calculate_num_cdf_axis100(data_gpu_mem)\n",
+    "\n",
+    "with open(f\"{SAVEPKL}/util_gpu_util_mem_seren.pkl\", \"wb\") as file:\n",
+    "    pickle.dump([x1, y1, x2, y2], file)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Processing IPMI Power Files"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_AB = pd.read_csv(\"./ipmi/GPU_AB_Power.csv\", parse_dates=[\"Time\"])\n",
+    "df_C = pd.read_csv(\"./ipmi/GPU_C_Power.csv\", parse_dates=[\"Time\"])\n",
+    "df_D = pd.read_csv(\"./ipmi/CPU_D_Power.csv\", parse_dates=[\"Time\"])\n",
+    "\n",
+    "df_A = df_AB.dropna()\n",
+    "df_B = df_AB[df_AB.isna().any(axis=1)]  # Type B without MEM_Power record\n",
+    "\n",
+    "dfs = {\"GPU_A\": df_A, \"GPU_B\": df_B, \"GPU_C\": df_C, \"CPU_D\": df_D}\n",
+    "\n",
+    "# Extract sys_total_power\n",
+    "df_A_power = df_A[[\"Time\", \"Sys_Total_Power\"]]\n",
+    "df_B_power = df_B[[\"Time\", \"Sys_Total_Power\"]]\n",
+    "df_C_power = df_C[[\"Time\", \"Sys_Total_Power\"]]\n",
+    "df_gpu = pd.concat([df_A_power, df_B_power, df_C_power])\n",
+    "\n",
+    "x1, y1 = calculate_sum_cdf_axis100(df_gpu)\n",
+    "x2, y2 = calculate_sum_cdf_axis100(df_D[[\"Time\", \"Sys_Total_Power\"]])\n",
+    "\n",
+    "with open(f\"./server_power.pkl\", \"wb\") as file:\n",
+    "    pickle.dump([x1, y1, x2, y2], file)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Processing Philly GPU Utilization Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "philly = pd.read_csv(\n",
+    "    \"./philly/philly_gpu_util.csv\", on_bad_lines=\"skip\", header=0\n",
+    ")  # Please refer to their official repo for the data\n",
+    "cols = list(philly.columns)\n",
+    "philly = philly.drop(columns=[cols[-1]])\n",
+    "philly.reset_index(inplace=True)\n",
+    "philly.columns = cols\n",
+    "philly.rename(columns={\"time\": \"Time\"}, inplace=True)\n",
+    "philly = philly.drop(columns=[cols[1]])\n",
+    "\n",
+    "x1, y1 = calculate_num_cdf_axis100(philly)\n",
+    "with open(f\"{SAVEPKL}/util_gpu_util_philly.pkl\", \"wb\") as file:\n",
+    "    pickle.dump([x1, y1], file)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.16"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

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