README.md

---
pretty_name: "LOOPerSet"
license: "cc-by-4.0"
tags:
- compilers
- code-optimization
- polyhedral-model
- performance-prediction
task_categories:
- other
size_categories:
- 10M<n<100M
configs:
- config_name: full
  data_files:
  - split: train
    path: "data/full/looperset_v2_full.jsonl.gz"
    
- config_name: full_compact
  data_files:
  - split: train
    path: "data/full/looperset_v2_full_compact.jsonl.gz"

- config_name: pact25_split
  data_files: 
  - split: train
    path: "data/pact25/looperset_v2_pact_train.jsonl.gz"
  - split: validation
    path: "data/pact25/looperset_v2_pact_validation.jsonl.gz"

- config_name: pact25_split_compact
  data_files: 
  - split: train
    path: "data/pact25/looperset_v2_pact_train_compact.jsonl.gz"
  - split: validation
    path: "data/pact25/looperset_v2_pact_validation_compact.jsonl.gz"
---

# LOOPerSet: A Large-Scale Dataset for Data-Driven Polyhedral Optimization

<div align="center">

[![Paper](https://img.shields.io/badge/LOOPerSet_Paper-arXiv-b31b1b.svg)](https://arxiv.org/abs/2510.10209)
[![PACT '25 Paper](https://img.shields.io/badge/LOOPer_Paper-PACT_'25-blue.svg)](https://ieeexplore.ieee.org/document/11282943)
[![License: CC BY 4.0](https://img.shields.io/badge/License-CC--BY%204.0-lightgrey.svg)](https://creativecommons.org/licenses/by/4.0/)
[![Dataset size](https://img.shields.io/badge/Dataset%20size-28M%20datapoints-brightgreen)]()

</div>


## Dataset at a Glance

`LOOPerSet` is a corpus of 28 million labeled compilation traces designed for machine learning research in compilers and systems. It maps synthetically generated loop nests and complex optimization sequences to ground-truth execution times measured on physical hardware. Transformation sequences were generated using a polyhedral compilation framework to ensure they were legal and semantics-preserving. 

`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).

### What is inside?
Each data point represents a **(Program, Schedule) &rarr; Performance** tuple containing:
*   **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.

### 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:
*   **Performance Prediction**: The dataset's primary use case.  Train a model to map a program's features and a candidate optimization schedule to a predicted performance value (e.g., execution time or speedup). This forms the core of a learned cost model for guiding compiler optimization.
*   **Schedule Ranking**: A learning-to-rank task where a model learns to order a set of candidate schedules for a given program based on their relative performance.
*   **Compiler Heuristic Discovery**: A data analysis task to discover new optimization heuristics by finding correlations between program features and the effectiveness of transformation sequences.
*   **Program Representation Learning**: Develop and evaluate novel methods for featurizing programs, computer code, and transformation schedules, such as learning dense vector embeddings.
*   **Transfer Learning**: A general-purpose cost model can be pre-trained on `LOOPerSet` and then fine-tuned on a much smaller, target-specific dataset, significantly reducing the data collection cost for new architectures.
  
  
### Dataset Configurations

The dataset is provided in two structural variants (**Standard** and **Compact**) across two split configurations (**Full** and **PACT '25**), plus a supplementary source code archive.


#### Variants
*   **Standard**: Contains complete program information including raw C++ code, lowered IRs, and compile commands.  Ideal for source code analysis and NLP tasks.
*   **Compact**: Optimized for speed and low-memory usage. It retains all features needed for training performance models but excludes raw code strings and intermediate representations. Recommended for training cost models and performance prediction.


#### Splits
*   **`full`**: The complete ~28 million point dataset (composed of ~220k programs), available as a single `train` split.
*   **`pact25`** split: A 10-million-point version used to train the LOOPer cost model, pre-split into `train` (90%) and `validation` (10%) sets for reproducibility. This is a subset of the Full dataset.

#### Generators Archive
 A compressed `tar.gz` containing the raw ~220k Tiramisu C++ generator files (`.cpp`). These match the `program_name` keys in the dataset and are useful for static analysis or if you wish to re-compile/re-execute the programs yourself.


#### File Sizes

| 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 |

## How to Use

The dataset files are stored in  `.jsonl.gz`  format (gzipped JSON Lines), where each line is a complete JSON object representing one program.

Below we provide a simple method to download the files and stream the data in Python.  

### Installation

  
You will need the `huggingface-hub` library to download the files from the repository.


```bash
pip install huggingface-hub
```


### Step 1: Download the Data Files

First, use the `hf_hub_download` function to fetch the dataset files you need.

```python
from huggingface_hub import hf_hub_download
import os

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",
    repo_type="dataset",
)
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",
    repo_type="dataset",
)
pact25_validation_path = hf_hub_download(
    repo_id=REPO_ID,
    filename="data/pact25/looperset_v2_pact_validation.jsonl.gz",
    repo_type="dataset",
)
print(f"PACT'25 train split downloaded to: {pact25_train_path}")
print(f"PACT'25 validation split downloaded to: {pact25_validation_path}")
```

### Step 2: Stream and Parse the Data

Due to the large size of the dataset, we recommend streaming the data using a generator function.

The following function reads a `.jsonl.gz` file line-by-line.

```python
import gzip
import json

def stream_jsonl_gz(file_path):
    """
    Generator function to stream and parse a .jsonl.gz file.
    Yields one JSON object (as a Python dict) at a time.
    """
    with gzip.open(file_path, 'rt', encoding='utf-8') as f:
        for line in f:
            yield json.loads(line)

# --- Example: Iterate through the pact25_split training set ---
# (Assuming you have run the download code from Step 1)
data_stream = stream_jsonl_gz(pact25_train_path)

print("First 3 programs from the stream:")
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"  Number of schedules: {len(program['schedules_list'])}")
```
  

### Example 1: Generating Training Examples


  

Each record in `LOOPerSet` represents a single **program**. This program contains a list of all **schedules** (optimization sequences) that were evaluated for it. To create training examples, one must iterate through each program and then through its `schedules_list`.


Here is how you can use the streamer to create `(program, schedule, performance)` tuples.


```python
import numpy as np

# (pact25_train_path is defined in the download step)
data_stream = stream_jsonl_gz(pact25_train_path)

training_examples = []

for processed_count, program in enumerate(data_stream):
		# iterate over the first 100 programs only
    if processed_count >= 100:
        break

    program_features = program['program_annotation']
    initial_time = program['initial_execution_time']

    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
        # Here we compute speedup over the un-optimized version
        median_time = np.median(schedule['execution_times'])
                 
        speedup = initial_time / median_time
        
        training_examples.append({
            "program_features": program_features,
            "schedule_features": schedule_features,
            "speedup": speedup
        })

print(f"Created {len(training_examples)} tuples from {processed_count} programs.")
```


###  Example 2: Finding the Best Schedule per Program 
The following example shows how to find the best speedup achieved for each program:

  
```python
import numpy as np

# (pact25_train_path is defined in the download step)
data_stream = stream_jsonl_gz(pact25_train_path)

# Iterate through a few programs and find the best schedule for each
num_programs_to_process = 5
processed_count = 0


# Iterate through a few programs and find the best schedule for each
for processed_count, program in enumerate(data_stream):
    if processed_count >= 5:
        break

    program_name = program['program_name']
    initial_time = program['initial_execution_time']

    # Handle cases where the initial run might have failed
    if initial_time is None:
        print(f"\nProgram: {program_name} has no initial time. 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'):
            continue
            
        current_time = np.median(schedule['execution_times'])
        
        if current_time < min_time:
            min_time = current_time
            best_schedule_info = schedule['schedule_str']

    speedup = initial_time / min_time if min_time > 0 else float('inf')

    print(f"\nProgram: {program_name}")
    print(f"  - Initial Time: {initial_time:.4f} ms")
    if best_schedule_info:
        print(f"  - Best Found Time: {min_time:.4f} ms (Speedup: {speedup:.2f}x)")
        print(f"  - Best Schedule: {best_schedule_info}")
    else:
        print("  - No better schedule found in the dataset.")
    
```


## Dataset Structure

  

Each row in the dataset represents a single synthetic program and contains all optimization schedules explored for it.

  

<details>

<summary><b>Click to see a sample JSONL entry</b></summary>

  

```json
{
  "program_name": "function12345",
  "program_annotation": {
    "memory_size": 4.19,
    "iterators": { "...": "..." },
    "computations": { "...": "..." },
    "buffers": { "...": "..." }
  },
  "Tiramisu_cpp": "// raw tiramisu generator source code ...",
  "initial_execution_time": 1393.751,
  "schedules_list": [
    {
      "transformations_list": [
        {"type": "interchange", "loop_levels": [0,1], "parameters": [], "computations": ["comp00"]},
        {"type": "tiling", "loop_levels": [1,2], "parameters": [32,32], "computations": ["comp01","comp02"]}
      ],
      "schedule_str": "I(L0,L1,comps=[comp00])|T2(L1,L2,32,32,comps=[comp02,comp02])",
      "legacy_schedule_str": "I({C0},L0,L1)T2({C1,C2},L2,L3,32,32)...",
      "ISL_AST": "..." ,
      "Halide_IR": "// lowered Halide IR ...",
      "Tiramisu_transform_commands": "comp01.tile(...); comp00.interchange(...); ...",
      "execution_times": [451.234, 465.112, 458.543, ...]
    },
    { /* ... another schedule object ... */ }
  ]
}

```
</details>

  


### Top-Level Fields

*   `program_name` (string): A unique identifier for the synthetic program (e.g., "function684979"). This name is also used to locate the corresponding generator file in the source archive:
  `<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.
*   `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)**.

---

### The `program_annotation` Dictionary

This object contains all the static information about the source program.

*   `memory_size` (float): The total memory footprint of all buffers in megabytes.
*   `iterators` (dict): Contains the full loop nest hierarchy of the program. Each key is an iterator name (e.g., `i0`), and the value contains its `lower_bound`, `upper_bound`, `parent_iterator`, and `child_iterators`.
*   `computations` (dict): Contains all computational statements. Each key is a computation name (e.g., `comp00`), and the value contains its properties, including:
    *   `iterators`: The list of loops this computation is nested in.
    *   `write_access_relation`: A string representing the write access pattern.
    *   `accesses`: A list of all read memory accesses.
    *   `expression_representation`: A tree-based representation of the arithmetic expression.
*   `buffers` (dict): Contains metadata for all data arrays (buffers) used in the program, including their dimensions, data types, and whether they are inputs or outputs.

---

### The `schedules_list` Entries

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).
*  `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)**.
- `ISL_AST` (string): An ISL (Integer Set Library) abstract syntax tree representing the loop nest after the transformation is applied.  **(Excluded in Compact version)**.
- `Halide_IR` (string): Generated/lowered Halide IR after applying the transformations.  **(Excluded in Compact version)**.
- `Tiramisu_transform_commands` (string): The actual Tiramisu C++ API commands used to apply this schedule.  **(Excluded in Compact version)**.



####  Transformation Object Format (`transformations_list`)

Each item in the `transformations_list` is a dictionary describing a single step:

```json
{
  "type": "String",              // e.g., "skewing", "interchange", "tiling", etc.
  "loop_levels": [Integers],      // List of loop levels involved (e.g., [0,1])
  "parameters": [Integers],       // Numeric parameters (tiling factors, skewing coefficients)
  "computations": ["String"]     // List of computation IDs affected
}
```

#### Schedule String Format (`schedule_str`)

A schedule is represented as a pipe-separated list of transformations:

`<T1>|<T2>|<T3>|...`

Supported transformations:

- `S(LX,LY,v1,v2,comps=[...])`: skewing loop levels `LX` and `LY` with factors `v1`, `v2`
- `I(LX,LY,comps=[...])`: interchange loop levels `LX` and `LY`
- `R(LX,comps=[...])`: reversal of loop level `LX`
- `P(LX,comps=[...])`: parallelization of loop level `LX`
- `T2(LX,LY,v1,v2,comps=[...])`: 2D tiling of loop levels `LX`,`LY` with factors `v1`,`v2`
- `T3(LX,LY,LZ,v1,v2,v3,comps=[...])`: 3D tiling of loop levels `LX`,`LY`,`LZ` with factors `v1`,`v2`,`v3`
- `U(LX,v,comps=[...])`: unrolling of loop level `LX` with factor `v`
- `F(LX,comps=[...])`: fusion at loop level `LX` for the computations listed in `comps`


## 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.

*   **Content:** Contains ~220,000 `.cpp` files.
*   **Filename Format:** `<program_name>_generator.cpp` (e.g., `function12345_generator.cpp`).
*   **Relation to JSON:** The content of these files is identical to the string found in the `Tiramisu_cpp` field within the JSON dataset. The archive is provided purely for convenience.

**How to extract specific files in Python:**

```python
import tarfile

# Extract a specific generator file
with tarfile.open(source_code_path, "r:gz") as tar:
    # Example: Extract function12345_generator.cpp
    member = tar.getmember("looperset_generators/function793216_generator.cpp")
    f = tar.extractfile(member)
    content = f.read().decode('utf-8')
    print(content)
```


## Dataset Creation

### Generation Pipeline

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.

### Diversity Analysis


A quantitative diversity analysis was performed to validate the dataset's quality. Using normalized Tree Edit Distance (nTED) to measure structural similarity between programs, the analysis showed that:
1.  `LOOPerSet` does not contain any accidental replications of PolyBench benchmarks.
2.  The dataset covers a broader and more varied structural space than existing benchmark suites.

Full details are available in our [companion paper](https://arxiv.org/abs/2510.10209).

## Citation Information

If you use this dataset, please cite the following paper:

```bibtex
@misc{looperset,
      title={LOOPerSet: A Large-Scale Dataset for Data-Driven Polyhedral Compiler Optimization}, 
      author={Massinissa Merouani and Afif Boudaoud and Riyadh Baghdadi},
      year={2025},
      eprint={2510.10209},
      archivePrefix={arXiv},
      primaryClass={cs.PL},
      url={https://arxiv.org/abs/2510.10209}, 
}
```

If you a building upon or comparing against the `LOOPer` cost model, please cite our PACT '25 paper:

```bibtex
@INPROCEEDINGS{looper,
  author={Merouani, Massinissa and Boudaoud, Afif and Aouadj, Iheb Nassim and Tchoulak, Nassim and Bernou, Islem Kara and Benyamina, Hamza and Tayeb, Fatima Benbouzid-Si and Benatchba, Karima and Leather, Hugh and Baghdadi, Riyadh},
  booktitle={2025 34th International Conference on Parallel Architectures and Compilation Techniques (PACT)}, 
  title={LOOPer: A Learned Automatic Code Optimizer For Polyhedral Compilers}, 
  year={2025},
  pages={201-215},
  keywords={Deep learning;Costs;Codes;Program processors;Predictive models;Space exploration;Parallel architectures;Optimization;Pluto;Faces;Compilers;Optimization;Program transformation;Machine learning;Modeling techniques},
  doi={10.1109/PACT65351.2025.00028}}

```



## License

This dataset is licensed under the [Creative Commons Attribution 4.0 International (CC-BY 4.0) License](https://creativecommons.org/licenses/by/4.0/).


## Versioning / Changelog

**v2.0  Schema Update & Compact Splits**
*   **Source Code Availability:** Added raw Tiramisu (C++) generator code to both the JSON dataset (`Tiramisu_cpp` field) and as a standalone downloadable archive.
*   **Compact Mode:** Introduced "Compact" dataset configurations that strip out large text fields (Source, IR, AST) to optimize for speed when training standard performance models.
*   **Schema Update:** Completely restructured the `schedules_list`. Replaced ad-hoc lists with a structured `transformations_list` dictionary format and standardized the schedule string representation.
*   **PACT '25 Update:** Updated citation information for the published LOOPer paper.

**v1.0 (Initial Release)**
*   Original dataset release containing ~220k programs and 28M schedules.
*   Includes `full` and `pact25` split configurations.