| # PhD-Level Hardware/Software Codesign Tasks for Syscraft | |
| Research notes on designing PhD-level evaluation tasks that exercise system-level hardware/software codesign on bare-metal (CloudLab) and VM environments. | |
| ## Motivation | |
| CloudLab / bare-metal environments enable a class of evaluation tasks that Docker-based setups cannot support: kernel boot parameters, IRQ affinity, NUMA topology, NVMe namespace control, NIC offloads, CXL memory tiering, and so on. This makes them the natural target for **PhD-level HW/SW codesign tasks** where the agent must reason across the system stack rather than tune a single knob. | |
| The distinguishing property of a PhD-level task should be that it lives on a **Pareto frontier** (performance vs cost vs reliability) — single-knob optimization should not be sufficient. The agent must perform genuine cross-layer reasoning about workload characteristics interacting with device queue models, NUMA topology, scheduler behavior, etc. | |
| | Level | Goal | Example | | |
| |-------|------|---------| | |
| | Sysadmin | Fix the broken thing (recovery) | Restore a split-brain Cassandra cluster | | |
| | Engineer | Make it work (config) | Bring up a new service with reasonable defaults | | |
| | **PhD** | **Find the optimal point on the perf/cost/reliability frontier via deep HW/SW codesign** | **See task categories below** | | |
| ## Candidate Task Categories (Ranked) | |
| ### Tier 1 — Highest potential, runnable on CloudLab today | |
| #### 1. NVMe storage stack codesign | |
| Given a mixed read/write workload (e.g., RocksDB compaction + foreground OLTP), the agent jointly tunes: | |
| - NVMe queue depth and namespace configuration | |
| - `io_uring` vs `libaio` | |
| - Block I/O scheduler (`mq-deadline` / `kyber` / `bfq`) | |
| - Filesystem choice (XFS / ext4 / btrfs) and mount options (`noatime`, `discard`, etc.) | |
| - cgroup v2 `io.weight` | |
| **Scoring:** p99.9 latency at fixed throughput, plus IOPS efficiency (IOPS / CPU%). The difficulty is that the agent must reason about the workload access pattern interacting with the device queue model. | |
| #### 2. Network dataplane codesign | |
| The agent implements an L4 LB or packet filter using DPDK / XDP / AF_XDP, jointly designing: | |
| - CPU isolation (`isolcpus` / `nohz_full`) | |
| - NIC RSS / RFS queue steering | |
| - IRQ affinity | |
| - NUMA-local memory pools | |
| **Scoring:** maximum pps at fixed p99 latency (< X µs), plus jitter standard deviation. Particularly PhD-level because tuning any single axis alone fails to score. | |
| #### 3. NUMA + memory tiering codesign | |
| For an in-memory analytics workload (DuckDB / ClickHouse), the agent jointly configures: | |
| - THP `madvise` vs `always` | |
| - `numactl` interleave policy | |
| - cgroup `memory.high` | |
| - (If available) CXL / PMEM hot/cold tiering | |
| **Scoring:** scan latency p99 + memory bandwidth utilization. | |
| ### Tier 2 — Compelling but require specific hardware | |
| #### 4. RDMA + NCCL collective tuning | |
| Requires InfiniBand or RoCE. The agent configures GPUDirect, `NCCL_TOPO`, QP parameters for multi-node allreduce. **Scoring:** bus bandwidth as fraction of peak. Worthwhile if CloudLab nodes have IB. | |
| #### 5. Real-time / SCHED_DEADLINE jitter optimization | |
| Bootloader parameters (`isolcpus` + `rcu_nocbs` + `nohz_full`) + IRQ steering + SCHED_DEADLINE budget. **Scoring:** cyclictest p99.99 jitter. Narrow scope but very PhD-flavored. | |
| ## Scoring Framework | |
| A multi-objective approach with a baseline ratio works well across all categories: | |
| - **Baseline:** naive default configuration (vanilla Linux defaults) | |
| - **Oracle ceiling:** hand-tuned expert configuration from a published artifact | |
| - **Score:** `(agent - baseline) / (oracle - baseline)` | |
| - **SLA gate:** hard constraint (e.g., p99 < X) that must be satisfied for any positive score | |
| This cleanly separates "configured correctly" from "genuinely approached the expert ceiling." | |
| ## Reference Papers | |
| The systems community has published detailed hand-tuned configurations that can be reused directly as oracle baselines. | |
| ### 1. NVMe / Storage stack | |
| **Design space and baselines:** | |
| - **KVell** (Lepers et al., SOSP '19) — Demonstrates LSM CPU bottlenecks on NVMe; gives RocksDB hand-tuned baseline numbers. | |
| - **SILK** (Balmau et al., ATC '19) — RocksDB tail-latency tuning with compaction I/O scheduling oracle numbers. | |
| - **uFS / SplitFS** (Kadekodi et al., SOSP '19) — Userspace FS vs kernel ext4/XFS; hand-tuned upper bound. | |
| - **ScaleXFS** (Kim et al., FAST '22) — XFS scalability ceiling under multi-core NVMe. | |
| **Auto-tuning frameworks (useful as agent baselines):** | |
| - **OtterTune** (Van Aken et al., SIGMOD '17) — Automated DBMS configuration with expert-DBA vs auto-tuned comparison tables. | |
| - **LlamaTune** (Kanellis et al., VLDB '22) — Sample-efficient improvement over OtterTune; clean design-space description. | |
| - **CherryPick** (Alipourfard et al., NSDI '17) — Bayesian-opt cloud configuration including VM type + storage tier. | |
| ### 2. Network dataplane | |
| The most mature category — nearly every SOSP/OSDI paper here provides hand-tuned numbers: | |
| - **IX** (Belay et al., OSDI '14) — Foundational dataplane OS; provides Linux / mTCP / IX baseline comparison. | |
| - **ZygOS** (Prekas et al., SOSP '17) — Work-stealing for µs-scale latency; compared against IX. | |
| - **Shenango** (Ousterhout et al., NSDI '19) — 5 µs core reallocation; includes detailed `isolcpus` + IRQ steering configuration. | |
| - **Caladan** (Fried et al., OSDI '20) — Interference mitigation. **Especially recommended** because the evaluation section lists all hand-tuned IRQ + cgroup configurations. | |
| - **Snap** (Marty et al., SOSP '19) — Google's production network stack with Pony Express tuning details. | |
| - **eRPC** (Kalia et al., NSDI '19) — Extreme single-threaded RPC; ablation tables cover NIC offload / DPDK parameters. | |
| - **Junction** (Fried et al., 2024) — Latest evolution of Caladan with fine-grained CPU sharing. | |
| - **XDP** (Høiland-Jørgensen et al., CoNEXT '18) — XDP vs DPDK vs kernel comparison baseline. | |
| ### 3. NUMA + memory tiering | |
| The hottest category in the past 3 years; CXL papers consistently include hand-tuned configurations: | |
| - **TMO** (Weiner et al., ASPLOS '22) — Meta production memory offloading; oracle numbers for PSI-based tuning. | |
| - **TPP** (Maruf et al., ASPLOS '23) — CXL tiered memory page placement. **Appendix contains a complete table of NUMA balancing + kswapd parameters.** | |
| - **Pond** (Li et al., ASPLOS '23) — Azure CXL memory pool with ML-driven configuration vs expert baseline. | |
| - **Memtis** (Lee et al., SOSP '23) — Dynamic page-size determination; especially complete design-space description. | |
| - **Mitosis** (Achermann et al., ASPLOS '20) — Page-table replication for NUMA; analyzes interaction with huge pages. | |
| ### 4. Surveys / system-level analyses | |
| Useful when defining the design space for a task spec: | |
| - **An Analysis of Performance Evolution of Linux's Core Operations** (Ren et al., SOSP '19) — Decade-long performance evolution of Linux core operations; reveals which knobs actually matter. | |
| - **Understanding PCIe Performance for End Host Networking** (Neugebauer et al., SIGCOMM '18) — Physical upper bound of PCIe + NIC interaction. | |
| - **My VM is Lighter (and Safer) than your Container** (Manco et al., SOSP '17) — Detailed decomposition of VM vs container overhead. | |
| ## Recommended Pattern for Task Design | |
| **Fork the artifacts of these papers directly:** | |
| 1. Use the authors' optimal configuration as the **oracle ceiling**. | |
| 2. Use default Linux as the **baseline**. | |
| 3. Leave N `sysctl` / cgroup / boot parameters as the agent's exploration surface. | |
| This makes the oracle upper bound paper-quality and immediately credible. | |
| ### Highest-priority papers to mine | |
| 1. **Caladan** + **Shenango** — Open-source artifacts ship with expert `isolcpus` / IRQ / cgroup scripts directly usable as an oracle ceiling. | |
| 2. **TPP** and **Memtis** — Appendices give complete `sysctl` + cgroup parameter sets. | |
| 3. **SILK** + **KVell** — RocksDB tuning with quantified baseline / expert / paper tiers. | |
| ## Personal Recommendation | |
| Most bullish on **(1) NVMe storage codesign** and **(2) DPDK/XDP network dataplane** — both have clear physical upper bounds (device queue model, PCIe bandwidth), demand genuine cross-layer reasoning, and run on common CloudLab profiles (c220g5 / xl170). | |
| A natural next step is to pick one paper artifact (e.g., Caladan) and prototype its conversion into a Syscraft CloudLab task: extract the expert configuration as oracle, define the SLA gate, and identify the exploration surface to expose to the agent. | |