pyaccelerate 0.2.0

High-performance Python acceleration engine — CPU, threads, virtual threads, multi-GPU, NPU and virtualization.

PyAccelerate

High-performance Python acceleration engine — CPU, threads, virtual threads, multi-GPU and virtualization.

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Features

Module	Description
cpu	CPU detection, topology, NUMA, affinity, ISA flags, dynamic worker recommendations
threads	Persistent virtual-thread pool, sliding-window executor, async bridge, process pool
gpu	Multi-vendor GPU detection (NVIDIA/CUDA, AMD/OpenCL, Intel oneAPI), ranking, multi-GPU dispatch
virt	Virtualization detection (Hyper-V, VT-x/AMD-V, KVM, WSL2, Docker, container detection)
memory	Memory pressure monitoring, automatic worker clamping, reusable buffer pool
profiler	@timed, @profile_memory decorators, Timer context manager, Tracker statistics
benchmark	Built-in micro-benchmarks (CPU, threads, memory bandwidth, GPU compute)
engine	Unified orchestrator — auto-detects everything and provides a single API

Quick Start

pip install pyaccelerate

from pyaccelerate import Engine

engine = Engine()
print(engine.summary())

# Submit I/O-bound tasks to the virtual thread pool
future = engine.submit(my_io_func, arg1, arg2)

# Run many tasks with auto-tuned concurrency
engine.run_parallel(process_file, [(f,) for f in files])

# GPU dispatch (auto-fallback to CPU)
results = engine.gpu_dispatch(my_kernel, data_chunks)

CLI

pyaccelerate info          # Full hardware report
pyaccelerate benchmark     # Run micro-benchmarks
pyaccelerate gpu           # GPU details
pyaccelerate cpu           # CPU details
pyaccelerate virt          # Virtualization info
pyaccelerate memory        # Memory stats
pyaccelerate status        # One-liner

Modules in Depth

Virtual Thread Pool

Inspired by Java's virtual threads — a persistent ThreadPoolExecutor sized for I/O (cores × 3, cap 32). All I/O-bound work shares this pool instead of creating/destroying threads per operation.

from pyaccelerate.threads import get_pool, run_parallel, submit

# Single task
fut = submit(download_file, url)

# Bounded concurrency (sliding window)
run_parallel(process, [(item,) for item in items], max_concurrent=8)

Multi-GPU Dispatch

Auto-detects GPUs across CUDA, OpenCL and Intel oneAPI. Distributes workloads with configurable strategies.

from pyaccelerate.gpu import detect_all, dispatch

gpus = detect_all()
results = dispatch(my_kernel, data_chunks, strategy="score-weighted")

Profiling

Zero-config decorators for timing and memory tracking:

from pyaccelerate.profiler import timed, profile_memory, Tracker

@timed(level=logging.INFO)
def heavy_computation():
    ...

tracker = Tracker("db_queries")
for batch in batches:
    with tracker.measure():
        run_query(batch)
print(tracker.summary())

Installation Options

# Core (CPU + threads + memory + virt)
pip install pyaccelerate

# With NVIDIA GPU support
pip install pyaccelerate[cuda]

# With OpenCL support (AMD/Intel/NVIDIA)
pip install pyaccelerate[opencl]

# With Intel oneAPI support
pip install pyaccelerate[intel]

# All GPU backends
pip install pyaccelerate[all-gpu]

# Development
pip install pyaccelerate[dev]

Docker

# CPU-only
docker build -t pyaccelerate .
docker run --rm pyaccelerate info

# With NVIDIA GPU
docker build -f Dockerfile.gpu -t pyaccelerate:gpu .
docker run --rm --gpus all pyaccelerate:gpu info

# Docker Compose
docker compose up pyaccelerate    # CPU
docker compose up gpu             # GPU

Development

git clone https://github.com/GuilhermeP96/pyaccelerate.git
cd pyaccelerate
pip install -e ".[dev]"

# Run tests
pytest -v

# Lint + format
ruff check src/ tests/
ruff format src/ tests/

# Type check
mypy src/

# Build wheel
python -m build

Architecture

pyaccelerate/
├── cpu.py          # CPU detection & topology
├── threads.py      # Virtual thread pool & executors
├── gpu/
│   ├── detector.py # Multi-vendor GPU enumeration
│   ├── cuda.py     # CUDA/CuPy helpers
│   ├── opencl.py   # PyOpenCL helpers
│   ├── intel.py    # Intel oneAPI helpers
│   └── dispatch.py # Multi-GPU load balancer
├── virt.py         # Virtualization detection
├── memory.py       # Memory monitoring & buffer pool
├── profiler.py     # Timing & profiling utilities
├── benchmark.py    # Built-in micro-benchmarks
├── engine.py       # Unified orchestrator
└── cli.py          # Command-line interface

Roadmap

• npm package (Node.js bindings via pybind11/napi)
• gRPC server mode for multi-language integration
• Kubernetes operator for auto-scaling GPU workloads
• Prometheus metrics exporter
• Auto-tuning feedback loop (benchmark → config → re-tune)

Origin

Evolved from the acceleration & virtual-thread systems built for:

• adb-toolkit — multi-GPU acceleration, virtual thread pool
• python-gpu-statistical-analysis — GPU compute foundations

License

MIT — see LICENSE.