v3.0: multi-target schema + Broadwell (xeon_e5_2680_v4) measurements; rename v2->v3

README.md

@@ -14,26 +14,26 @@ configs:
 - config_name: full
   data_files:
   - split: train
-    path: "data/full/looperset_v2_full.jsonl.gz"
+    path: "data/full/looperset_v3_full.jsonl.gz"
     
 - config_name: full_compact
   data_files:
   - split: train
-    path: "data/full/looperset_v2_full_compact.jsonl.gz"
+    path: "data/full/looperset_v3_full_compact.jsonl.gz"
 
 - config_name: pact25_split
   data_files: 
   - split: train
-    path: "data/pact25/looperset_v2_pact_train.jsonl.gz"
+    path: "data/pact25/looperset_v3_pact_train.jsonl.gz"
   - split: validation
-    path: "data/pact25/looperset_v2_pact_validation.jsonl.gz"
+    path: "data/pact25/looperset_v3_pact_validation.jsonl.gz"
 
 - config_name: pact25_split_compact
   data_files: 
   - split: train
-    path: "data/pact25/looperset_v2_pact_train_compact.jsonl.gz"
+    path: "data/pact25/looperset_v3_pact_train_compact.jsonl.gz"
   - split: validation
-    path: "data/pact25/looperset_v2_pact_validation_compact.jsonl.gz"
+    path: "data/pact25/looperset_v3_pact_validation_compact.jsonl.gz"
 ---
 
 # LOOPerSet: A Large-Scale Dataset for Data-Driven Polyhedral Optimization
@@ -55,6 +55,8 @@ configs:
 
 `LOOPerSet` was originally created to train the cost model for the [LOOPer autoscheduler](https://ieeexplore.ieee.org/document/11282943) (PACT '25). For a full description of the generation process and a diversity analysis, please see our [companion paper on arXiv](https://arxiv.org/abs/2510.10209).
 
+> **A living dataset.** `LOOPerSet` continues to grow over time — both with additional **hardware targets** (the same programs and schedules re-measured on new CPU/toolchain configurations) and with additional programs. Execution times are therefore keyed by a hardware **target ID**. This dataset card is the up-to-date reference for what each release contains; see [Changelog](#versioning--changelog).
+
 An open-source implementation of that cost model is available at [**LOOPerCostModel**](https://github.com/Mascinissa/LOOPerCostModel). It trains directly on `LOOPerSet` and reproduces the results reported in the LOOPer paper, providing a ready-to-use baseline for performance prediction and schedule ranking on this dataset.
 
 ### What is inside?
@@ -62,7 +64,7 @@ Each data point represents a **(Program, Schedule) → Performance** tuple co
 *   **Source Code & IR:** Raw Tiramisu (C++) generator code, lowered Halide IR, and ISL ASTs.
 *   **Structured Features:** JSON-based representation of the program structure (loop hierarchy, memory access patterns, arithmetic expressions) for feature engineering.
 *   **Optimization Schedules:** Sequences of code transformations (tiling, skewing, fusion, interchange, unrolling, parallelization, etc) and the specific API commands used to apply them.
-*   **Ground Truth:** Execution time (ms) measured over many runs on physical hardware.
+*   **Ground Truth:** Execution time (ms) measured over many runs on physical hardware, recorded per **hardware target** (see [Hardware Targets](#hardware-targets)).
 
 ### Key Research Tasks
 By exposing both low-level source code and high-level structural features, the dataset can be used for several research applications in machine learning and compilers:
@@ -95,13 +97,26 @@ The dataset is provided in two structural variants (**Standard** and **Compact**
 
 | Configuration | File Path | Compressed Size | Decompressed Size |
 | :--- | :--- | :--- | :--- |
-| **Full (Standard)** | `data/full/looperset_v2_full.jsonl.gz` | 6.0 GB | 84 GB |
-| **Full (Compact)** | `data/full/looperset_v2_full_compact.jsonl.gz` | 3.1 GB | 21 GB |
-| **PACT Train (Standard)** | `data/pact25/looperset_v2_pact_train.jsonl.gz` | 2.0 GB | 28 GB |
-| **PACT Train (Compact)** | `data/pact25/looperset_v2_pact_train_compact.jsonl.gz` | 1.1 GB | 6.8 GB |
-| **PACT Val (Standard)** | `data/pact25/looperset_v2_pact_validation.jsonl.gz` | 236 MB | 3.3 GB |
-| **PACT Val (Compact)** | `data/pact25/looperset_v2_pact_validation_compact.jsonl.gz` | 121 MB | 818 MB |
-| **Generators Source** | `data/source/looperset_v2_generators.tar.gz` | 34 MB | 339 MB |
+| **Full (Standard)** | `data/full/looperset_v3_full.jsonl.gz` | 8.2 GB | 91 GB |
+| **Full (Compact)** | `data/full/looperset_v3_full_compact.jsonl.gz` | 5.2 GB | 28 GB |
+| **PACT Train (Standard)** | `data/pact25/looperset_v3_pact_train.jsonl.gz` | 2.7 GB | 30 GB |
+| **PACT Train (Compact)** | `data/pact25/looperset_v3_pact_train_compact.jsonl.gz` | 1.7 GB | 9.2 GB |
+| **PACT Val (Standard)** | `data/pact25/looperset_v3_pact_validation.jsonl.gz` | 329 MB | 3.5 GB |
+| **PACT Val (Compact)** | `data/pact25/looperset_v3_pact_validation_compact.jsonl.gz` | 209 MB | 1.1 GB |
+| **Generators Source** | `data/source/looperset_v3_generators.tar.gz` | 34 MB | 339 MB |
+
+## Hardware Targets
+
+Every execution time in the dataset is recorded against a **hardware target**. The `execution_times` field (per schedule) and the `initial_execution_time` field (per program) are dictionaries keyed by the target IDs below.
+
+| Target ID | CPU / microarch | Tiramisu commit |
+|---|---|---|
+| `xeon_e5_2695_v2` | Intel Xeon E5-2695 v2 (Ivy Bridge) | `eab1502` (LLVM 5.0.2, Halide `release_2017_10_30`) |
+| `xeon_e5_2680_v4` | Intel Xeon E5-2680 v4 (Broadwell) | `041afad` (LLVM 13, Halide 14) |
+
+> **A target ID denotes a hardware _and_ toolchain configuration**, not just a CPU. Re-measuring the same CPU under a different compiler/runtime stack would be published under a new, suffixed target ID rather than overwriting an existing one.
+
+A target key is present for a given schedule or program **if and only if** that target has a measurement for it.
 
 ## How to Use
 
@@ -109,6 +124,14 @@ The dataset files are stored in  `.jsonl.gz`  format (gzipped JSON Lines), where
 
 Below we provide a simple method to download the files and stream the data in Python.  
 
+**Selecting a hardware target.** Because timings are keyed by target ID (see [Hardware Targets](#hardware-targets)), the examples below read a single target through one variable. Set it once and every example follows:
+
+```python
+TARGET = "xeon_e5_2695_v2"
+```
+
+Access the measurements as `schedule['execution_times'][TARGET]` and `program['initial_execution_time'][TARGET]`. Not every target measured every schedule, so use `.get(TARGET)` when a target's coverage may be partial (e.g. Broadwell).
+
 ### Installation
 
   
@@ -133,7 +156,7 @@ REPO_ID = "Mascinissa/LOOPerSet"
 # --- Option 1: Download the Full Compact (Recommended for Speed) ---
 full_compact_path = hf_hub_download(
     repo_id=REPO_ID,
-    filename="data/full/looperset_v2_full_compact.jsonl.gz",
+    filename="data/full/looperset_v3_full_compact.jsonl.gz",
     repo_type="dataset",
 )
 print(f"Full Compact dataset downloaded to: {full_compact_path}")
@@ -142,12 +165,12 @@ print(f"Full Compact dataset downloaded to: {full_compact_path}")
 # --- Option 2: Download the Standard PACT '25 splits ---
 pact25_train_path = hf_hub_download(
     repo_id=REPO_ID,
-    filename="data/pact25/looperset_v2_pact_train.jsonl.gz",
+    filename="data/pact25/looperset_v3_pact_train.jsonl.gz",
     repo_type="dataset",
 )
 pact25_validation_path = hf_hub_download(
     repo_id=REPO_ID,
-    filename="data/pact25/looperset_v2_pact_validation.jsonl.gz",
+    filename="data/pact25/looperset_v3_pact_validation.jsonl.gz",
     repo_type="dataset",
 )
 print(f"PACT'25 train split downloaded to: {pact25_train_path}")
@@ -182,7 +205,7 @@ for i, program in enumerate(data_stream):
     if i >= 3:
         break
     print(f"\n--- Program {i+1}: {program['program_name']} ---")
-    print(f"  Initial time: {program['initial_execution_time']:.4f} ms")
+    print(f"  Initial time ({TARGET}): {program['initial_execution_time'][TARGET]:.4f} ms")
     print(f"  Number of schedules: {len(program['schedules_list'])}")
 ```
   
@@ -212,15 +235,22 @@ for processed_count, program in enumerate(data_stream):
         break
 
     program_features = program['program_annotation']
-    initial_time = program['initial_execution_time']
+    initial_time = program['initial_execution_time'].get(TARGET)
+    if initial_time is None:
+        continue  # this target has no baseline measurement for this program
 
     for schedule in program['schedules_list']:
         schedule_features = schedule # Or a subset of its fields
-        
-        # The label is the median of the 30 execution times
+
+        # Skip schedules this target did not measure
+        times = schedule['execution_times'].get(TARGET)
+        if not times:
+            continue
+
+        # The label is the median of the (up to 30) execution times
         # Here we compute speedup over the un-optimized version
-        median_time = np.median(schedule['execution_times'])
-                 
+        median_time = np.median(times)
+
         speedup = initial_time / median_time
         
         training_examples.append({
@@ -254,22 +284,23 @@ for processed_count, program in enumerate(data_stream):
         break
 
     program_name = program['program_name']
-    initial_time = program['initial_execution_time']
+    initial_time = program['initial_execution_time'].get(TARGET)
 
-    # Handle cases where the initial run might have failed
+    # Handle cases where the initial run might have failed / this target is missing
     if initial_time is None:
-        print(f"\nProgram: {program_name} has no initial time. Skipping.")
+        print(f"\nProgram: {program_name} has no initial time for {TARGET}. Skipping.")
         continue
 
     best_schedule_info = None
     min_time = initial_time
 
     for schedule in program['schedules_list']:
-        # Ensure execution times are valid before calculating median
-        if not schedule.get('execution_times'):
+        # Ensure this target measured this schedule before calculating median
+        times = schedule['execution_times'].get(TARGET)
+        if not times:
             continue
-            
-        current_time = np.median(schedule['execution_times'])
+
+        current_time = np.median(times)
         
         if current_time < min_time:
             min_time = current_time
@@ -288,6 +319,29 @@ for processed_count, program in enumerate(data_stream):
 ```
 
 
+###  Example 3: Reading a Schedule Across Hardware Targets
+Because `execution_times` is a dict keyed by target ID, you can read the same schedule's performance on every target it was measured on:
+
+```python
+import numpy as np
+
+# (pact25_train_path is defined in the download step)
+data_stream = stream_jsonl_gz(pact25_train_path)
+program = next(data_stream)
+
+# Pick an optimized schedule and compare it across all targets present
+schedule = program['schedules_list'][10]
+
+print(f"Program: {program['program_name']}")
+for target_id, times in schedule['execution_times'].items():
+    print(f"  {target_id}: median = {np.median(times):.4f} ms  ({len(times)} runs)")
+
+# Baseline (un-optimized) time is likewise available per target
+for target_id, t in program['initial_execution_time'].items():
+    print(f"  initial [{target_id}]: {t:.4f} ms")
+```
+
+
 ## Dataset Structure
 
   
@@ -312,7 +366,7 @@ Each row in the dataset represents a single synthetic program and contains all o
     "buffers": { "...": "..." }
   },
   "Tiramisu_cpp": "// raw tiramisu generator source code ...",
-  "initial_execution_time": 1393.751,
+  "initial_execution_time": { "xeon_e5_2695_v2": 1393.751, "xeon_e5_2680_v4": 1201.4 },
   "schedules_list": [
     {
       "transformations_list": [
@@ -324,7 +378,10 @@ Each row in the dataset represents a single synthetic program and contains all o
       "ISL_AST": "..." ,
       "Halide_IR": "// lowered Halide IR ...",
       "Tiramisu_transform_commands": "comp01.tile(...); comp00.interchange(...); ...",
-      "execution_times": [451.234, 465.112, 458.543, ...]
+      "execution_times": {
+        "xeon_e5_2695_v2": [451.234, 465.112, 458.543, ...],
+        "xeon_e5_2680_v4": [388.4, 391.0, ...]
+      }
     },
     { /* ... another schedule object ... */ }
   ]
@@ -342,7 +399,7 @@ Each row in the dataset represents a single synthetic program and contains all o
   `<program_name>_generator.cpp`.
 *   `program_annotation` (dict): A detailed, structured representation of the original, untransformed program. This serves as the primary source for program feature engineering.
 *   `Tiramisu_cpp` (string): Raw Tiramisu generator C++ source code of the program before any schedule transformations. **(Excluded in Compact version)**
-*   `initial_execution_time` (float): The median execution time (in ms) of the program before any optimizations.
+*   `initial_execution_time` (dict): Maps each hardware **target ID** to the program's baseline execution time (in ms) before any optimizations. A target key is present only if that target has a baseline measurement for the program, e.g. `{"xeon_e5_2695_v2": 1393.751, "xeon_e5_2680_v4": 1201.4}`.
 *   `schedules_list` (list of dicts): A list of all optimization sequences explored for this program. Each dictionary in the list details a unique schedule and its performance.
 *   `exploration_trace` (dict): Internal search logs. **(Excluded in Compact version)**.
 
@@ -367,7 +424,7 @@ This object contains all the static information about the source program.
 
 Each element in this list represents one complete optimization schedule applied to the program.
 
-*   `execution_times` (list of float): A list of 30 raw execution time measurements (in ms) for this specific schedule. The ground-truth label for ML models is typically derived from this list (e.g., by taking the median).
+*   `execution_times` (dict): Maps each hardware **target ID** to a list of up to 30 raw execution time measurements (in ms) for this schedule on that target.  A target key is present only if that target measured this schedule. The ground-truth label for ML models is typically derived from a single target's list (e.g., by taking the median).
 *  `transformations_list` (list of dicts): A structured list where each element describes a specific transformation step (see format below).
 *   `schedule_str` (string): A human-readable summary string of the transformations applied in this schedule (see format below).
 - `legacy_schedule_str` (string): Legacy schedule string found in older versions of the dataset. **(Excluded in Compact version)**.
@@ -410,7 +467,7 @@ Supported transformations:
 
 ## C++ Source Code Archive
 
-This repository also includes a compressed archive containing the raw Tiramisu generator sources for all programs (`data/looperset_v2_generators.tar.gz`). These are provided to enable researchers to perform static program analysis, reproduce results by re-executing schedules on different hardware architectures, or extend the dataset by collecting completely new schedules.
+This repository also includes a compressed archive containing the raw Tiramisu generator sources for all programs (`data/source/looperset_v3_generators.tar.gz`). These are provided to enable researchers to perform static program analysis, reproduce results by re-executing schedules on different hardware architectures, or extend the dataset by collecting completely new schedules.
 
 *   **Content:** Contains ~220,000 `.cpp` files.
 *   **Filename Format:** `<program_name>_generator.cpp` (e.g., `function12345_generator.cpp`).
@@ -438,7 +495,7 @@ with tarfile.open(source_code_path, "r:gz") as tar:
 The data was generated using a three-stage pipeline:
 1.  **Synthetic Program Generation**: A randomized generator created a diverse corpus of polyhedral programs with varied loop structures, memory access patterns, and computational complexities.
 2.  **Transformation Space Sampling**: We used the beam search algorithm from the LOOPer autoscheduler to explore and sample meaningful optimization sequences for each program. This "relevance-guided" strategy ensures the dataset focuses on transformations a real-world compiler would consider.
-3.  **Performance Label Generation**: Each `(program, schedule)` pair was compiled with Tiramisu and executed on a dual-socket **Intel Xeon E5-2695 v2** system. Each version was run up 30 times to collect a stable distribution of execution times.
+3.  **Performance Label Generation**: Each `(program, schedule)` pair was compiled with Tiramisu and executed on physical hardware, running each version up to 30 times to collect a stable distribution of execution times. Labels were originally collected on a dual-socket **Intel Xeon E5-2695 v2** (Ivy Bridge) system and have since been extended to additional **hardware targets** — currently also an **Intel Xeon E5-2680 v4** (Broadwell) — by re-measuring the same programs and schedules under each target's CPU/toolchain configuration.
 
 ### Diversity Analysis
 
@@ -488,6 +545,13 @@ This dataset is licensed under the [Creative Commons Attribution 4.0 Internation
 
 ## Versioning / Changelog
 
+This card always documents the current release. `LOOPerSet` is a living dataset: expect future releases to add more hardware targets and more programs.
+
+**v3.0 — Multi-Target Schema & Broadwell Measurements (CGO '27 release)**
+*   **Multi-Target Schema (breaking):** `execution_times` (per schedule) and `initial_execution_time` (per program) are now **dictionaries keyed by a hardware target ID** instead of a bare list/float, respectively. This is a breaking, major-version change. See [Hardware Targets](#hardware-targets).
+*   **New Hardware Target — Broadwell:** Added Intel Xeon E5-2680 v4 (`xeon_e5_2680_v4`) execution-time measurements for the overlapping (program, schedule) pairs already present in the corpus, alongside the existing Ivy Bridge (`xeon_e5_2695_v2`) timings. A target key is present only where that target has a measurement; Broadwell coverage is high but partial.
+*   **File Renaming:** Data files renamed `looperset_v2_* → looperset_v3_*` to reflect the major-version bump; all download examples, configs, and the file-size table are updated accordingly.
+
 **v2.1 Minor bug fixes in annotations**
 
 **v2.0  Schema Update & Compact Splits**

data/full/{looperset_v2_full.jsonl.gz → looperset_v3_full.jsonl.gz}

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data/full/{looperset_v2_full_compact.jsonl.gz → looperset_v3_full_compact.jsonl.gz}

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data/pact25/{looperset_v2_pact_train.jsonl.gz → looperset_v3_pact_train.jsonl.gz}

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