pyaccelerate 0.10.0

High-performance Python acceleration engine — CPU, threads, virtual threads, multi-GPU, NPU, ARM/Android/Termux, IoT/SBC and virtualization.

PyAccelerate

High-performance Python acceleration engine — CPU, threads, virtual threads, multi-GPU, NPU, ARM/Android/Termux, IoT/SBC, auto-tuning, Prometheus metrics, HTTP/gRPC server, Kubernetes auto-scaling, and maximum optimization mode.

---

Features

Module	Description
cpu	CPU detection, topology, NUMA, affinity, ISA flags, ARM big.LITTLE/DynamIQ, dynamic worker recommendations
threads	Persistent virtual-thread pool, sliding-window executor, async bridge, process pool
work_stealing	Work-stealing scheduler (Tokio / Go runtime / ForkJoinPool style) — Chase-Lev deques, random victim selection
lockfree_queue	Lock-free MPMC queue & per-worker Chase-Lev deques for minimal contention
adaptive	Adaptive scheduler — auto-scales workers based on latency, CPU pressure & memory pressure
_native	Optional Cython / Rust (PyO3) accelerators for hot-path data structures
gpu	Multi-vendor GPU detection (NVIDIA/CUDA, AMD/OpenCL, Intel oneAPI, ARM Adreno/Mali/Immortalis), architecture classification (Kepler→Blackwell, GCN→RDNA4/CDNA3), CUDA/Tensor/RT core counts, NVENC/NVDEC & VCN encode/decode, clocks, memory type & bandwidth, PCIe info, driver version, shared VRAM for all vendors, Vulkan probing, ranking, multi-GPU dispatch
npu	NPU detection & inference (OpenVINO, ONNX Runtime, DirectML, CoreML, ARM Hexagon/Samsung NPU/Tensor TPU/MediaTek APU)
virt	Virtualization detection (Hyper-V, VT-x/AMD-V, KVM, WSL2, Docker, container detection)
memory	Memory pressure monitoring, automatic worker clamping, reusable buffer pool, GPU memory stats (dedicated + shared VRAM, CUDA/Tensor/RT cores, bandwidth, features)
profiler	@timed, @profile_memory decorators, Timer context manager, Tracker statistics
benchmark	Built-in micro-benchmarks (CPU, threads, memory bandwidth, GPU compute)
priority	OS-level task priority (IDLE → REALTIME) & energy profiles (POWER_SAVER → ULTRA_PERFORMANCE)
max_mode	Maximum optimization mode — activates ALL resources simultaneously with OS tuning
android	Android/Termux platform detection, ARM SoC database (25+ chipsets), big.LITTLE, thermal & battery
iot	IoT / SBC detection (Raspberry Pi, Jetson, BeagleBone, Coral, Hailo, 30+ SoCs)
autotune	Auto-tuning feedback loop — benchmark → config → re-tune, persistent profiles, hardware-safe limits & config clamping, GPU architecture/features/driver profiling
metrics	Prometheus metrics exporter (/metrics HTTP endpoint, all subsystems)
server	JSON HTTP & gRPC server for multi-language integration (Node.js, Go, Java, etc.)
k8s	Kubernetes operator — pod info, GPU node capacity, auto-scaling, manifest generation
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)

Benchmark

"Ok... but how much faster is it?" — Here are real numbers.

All benchmarks run on Python 3.12 / Windows with python -m benchmarks.run. IO/CPU/Mixed on 48-core Xeon; NPU on Intel Core Ultra 7 265U (Arrow Lake, 13 TOPS NPU). Reproduce: pip install pyaccelerate && python -m benchmarks.run

IO-bound — 200 simulated HTTP calls (20ms each)

Runner	Time	Speedup	Tasks/sec
Sequential	4.079s	1.0×	49
ThreadPoolExecutor	0.118s	34.7×	1,701
pyaccelerate.engine	0.193s	21.2×	1,038
pyaccelerate.threads	0.150s	27.3×	1,336
pyaccelerate.ws	0.240s	17.0×	832

IO-bound — 200 variable-latency calls (5–80ms, realistic)

Runner	Time	Speedup	Tasks/sec
Sequential	8.350s	1.0×	24
ThreadPoolExecutor	0.245s	34.1×	817
pyaccelerate.threads	0.329s	25.4×	608
pyaccelerate.ws	0.369s	22.6×	542
pyaccelerate.adaptive	0.656s	12.7×	305

CPU-bound — zlib compress 200KB × 100 (GIL released by C extension)

Runner	Time	Speedup	Tasks/sec
Sequential	0.153s	1.0×	654
ThreadPoolExecutor	0.049s	3.1×	2,026
pyaccelerate.threads	0.049s	3.1×	2,033
pyaccelerate.ws	0.079s	1.9×	1,264

Mixed — IO (10ms) + CPU (SHA-256 × 400) × 200

Runner	Time	Speedup	Tasks/sec
Sequential	2.216s	1.0×	90
ThreadPoolExecutor	0.197s	11.3×	1,017
pyaccelerate.threads	0.162s	13.7×	1,233
pyaccelerate.engine	0.206s	10.8×	972
pyaccelerate.adaptive	0.634s	3.5×	315

Key takeaway: pyaccelerate.threads beats ThreadPoolExecutor by 21% on mixed workloads. The work-stealing scheduler (ws) excels at variable-latency IO where load balancing matters most. Run your own benchmarks: python -m benchmarks.run (full) or python -m benchmarks.run --quick (CI).

NPU Inference — ONNX model, NPU (OpenVINO) vs CPU

Tested on Intel Core Ultra 7 265U (Arrow Lake) with Intel AI Boost NPU (13 TOPS) via OpenVINO. Models: multi-layer MatMul→ReLU MLP (1024→1024→512), single-stream inference.

Model	Runner	Avg latency	Speedup	Infer/sec
MLP-8L (shallow)	CPU (ONNX Runtime)	0.75 ms	1.0× (baseline)	1,341
MLP-8L (shallow)	NPU (OpenVINO)	1.66 ms	0.5×	603
MLP-16L	CPU (ONNX Runtime)	1.59 ms	1.0× (baseline)	630
MLP-16L	NPU (OpenVINO)	1.98 ms	0.8×	505
MLP-24L (deep)	CPU (ONNX Runtime)	16.24 ms	1.0× (baseline)	61
MLP-24L (deep)	NPU (OpenVINO)	4.69 ms	3.5×	213
MLP-32L (very deep)	CPU (ONNX Runtime)	8.70 ms	1.0× (baseline)	115
MLP-32L (very deep)	NPU (OpenVINO)	4.74 ms	1.8×	211

Key takeaway: The NPU excels at deep model inference — the deeper the model, the greater the NPU advantage. Shallow models (8–16 layers) see CPU win due to NPU dispatch overhead, but at 24+ layers the NPU delivers up to 3.5× speedup while freeing the CPU for other work. Run your own: python -m benchmarks.run --npu

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Maximum Optimization Mode

Activates all available hardware resources in parallel with OS-level tuning:

from pyaccelerate.max_mode import MaxMode

with MaxMode() as m:
    print(m.summary())  # hardware manifest

    # Run CPU + I/O simultaneously
    results = m.run_all(
        cpu_fn=cpu_heavy_task, cpu_items=cpu_data,
        io_fn=io_heavy_task, io_items=io_data,
    )

    # I/O only (thread pool)
    downloaded = m.run_io(download, [(url,) for url in urls])

    # CPU only (process pool)
    computed = m.run_cpu(crunch, [(n,) for n in numbers])

    # Multi-stage pipeline
    results = m.run_pipeline([
        ("download", download_fn, urls),
        ("transform", transform_fn, data),
        ("save", save_fn, output),
    ])

Or via the Engine:

engine = Engine()
with engine.max_mode() as m:
    results = m.run_all(...)

OS Priority & Energy Management

Control process scheduling and power profiles across Windows, Linux & macOS:

from pyaccelerate.priority import (
    TaskPriority, EnergyProfile,
    set_task_priority, set_energy_profile,
    max_performance, balanced, power_saver,
)

# Quick presets
max_performance()   # HIGH priority + ULTRA_PERFORMANCE energy
balanced()          # Restore defaults
power_saver()       # BELOW_NORMAL + POWER_SAVER

# Fine-grained control
set_task_priority(TaskPriority.ABOVE_NORMAL)
set_energy_profile(EnergyProfile.PERFORMANCE)

CLI

pyaccelerate info          # Full hardware report (+ offers to install missing deps)
pyaccelerate benchmark     # Run micro-benchmarks
pyaccelerate gpu           # GPU details (architecture, cores, VRAM, clocks, features, driver, PCIe)
pyaccelerate cpu           # CPU details
pyaccelerate npu           # NPU details
pyaccelerate android       # ARM/Android device details (SoC, clusters, thermal)
pyaccelerate virt          # Virtualization info
pyaccelerate memory        # Memory stats (system + GPU dedicated/shared)
pyaccelerate limits        # Hardware-safe batch/worker limits
pyaccelerate limits --json # Limits as JSON
pyaccelerate status        # One-liner
pyaccelerate priority      # Show current priority/energy
pyaccelerate priority --preset max     # Apply max performance preset
pyaccelerate priority --set high       # Set task priority
pyaccelerate priority --energy performance  # Set energy profile
pyaccelerate max-mode      # Show max-mode hardware manifest
pyaccelerate tune          # Auto-tune: benchmark → optimise → save
pyaccelerate tune --apply  # Tune and apply to current process
pyaccelerate tune --show   # Show current tune profile
pyaccelerate metrics       # Start Prometheus /metrics server (:9090)
pyaccelerate metrics --once# Print metrics and exit
pyaccelerate serve         # Start HTTP/gRPC API server (:8420)
pyaccelerate k8s           # Kubernetes pod & GPU info
pyaccelerate k8s --manifest# Generate K8s Deployment YAML
pyaccelerate iot           # IoT / SBC board details
pyaccelerate version       # Print version

ARM / Android / Termux Support

Full hardware detection for ARM devices — phones (Termux, Pydroid), tablets, Raspberry Pi, ARM laptops (Snapdragon X Elite), and ARM servers:

from pyaccelerate.android import (
    is_android, is_termux, is_arm,
    get_device_info, get_soc_info,
    detect_big_little, get_arm_features,
    get_thermal_zones, get_battery_info,
)

if is_arm():
    soc = get_soc_info()
    if soc:
        print(f"{soc.name} ({soc.vendor})")   # Snapdragon 8 Gen 3 (Qualcomm)
        print(f"GPU: {soc.gpu_name}")           # Adreno 750
        print(f"NPU: {soc.npu_name} ({soc.npu_tops} TOPS)")  # Hexagon NPU (73.0 TOPS)

    clusters = detect_big_little()
    # {"Cortex-X4": [0], "Cortex-A720": [1,2,3], "Cortex-A520": [4,5,6,7]}

    features = get_arm_features()
    # ["aes", "asimd", "bf16", "crc32", "neon", "sve", "sve2", ...]

Supported SoC families (25+ chipsets in database):

• Qualcomm — Snapdragon 8 Elite, 8/7/6 Gen 1-3, 888, 865, X Elite
• Samsung — Exynos 2500, 2200, 2100, 1380, 990
• Google — Tensor G1–G4
• MediaTek — Dimensity 9300, 9200, 9000, 8300, 1200, 1100, 900
• HiSilicon — Kirin 9010, 9000
• Unisoc — T616

ARM GPU detection — Adreno, Mali, Immortalis, Xclipse, PowerVR, Maleoon (via SoC DB, sysfs, Vulkan, OpenCL)

ARM NPU detection — Hexagon, Samsung NPU, Google TPU, MediaTek APU, Da Vinci NPU (via SoC DB, NNAPI, TFLite)

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)

Work-Stealing Scheduler

High-performance scheduler inspired by Tokio (Rust), Go runtime and Java ForkJoinPool. Each worker owns a local Chase-Lev deque — pops LIFO (cache-friendly), steals FIFO (fair). When idle, workers steal from random victims with exponential back-off parking.

from pyaccelerate.work_stealing import WorkStealingScheduler, ws_submit, ws_map

# Module-level convenience
fut = ws_submit(my_func, arg1, arg2)
results = ws_map(fn, [(a,), (b,), (c,)])

# Full control
with WorkStealingScheduler(num_workers=8, steal_batch_size=4) as sched:
    futures = [sched.submit(process, item) for item in items]
    results = [f.result() for f in futures]
    print(sched.stats())  # completed, stolen, avg_latency_us

Or via the Engine:

engine = Engine()
fut = engine.ws_submit(my_func, arg1)
results = engine.ws_map(fn, [(a,), (b,)])

Lock-Free Task Queue

The lockfree_queue module provides two data structures underlying the work-stealing scheduler:

• WorkDeque: Per-worker Chase-Lev deque — owner push/pop lock-free (GIL + collections.deque), stealers use a lightweight spinlock.
• MPMCQueue: Multi-Producer Multi-Consumer global injection queue with efficient Event-based parking.

from pyaccelerate.lockfree_queue import WorkDeque, MPMCQueue

# Per-worker deque
d = WorkDeque()
d.push(task)
task = d.pop()        # LIFO (owner)
task = d.steal()      # FIFO (other workers)
batch = d.steal_batch(4)

# Global queue
q = MPMCQueue()
q.put(task)
q.put_batch([t1, t2, t3])
task = q.get()
q.wait(timeout=1.0)   # block until items arrive

Adaptive Scheduler

Wraps the work-stealing scheduler and dynamically tunes worker count based on real-time metrics:

Signal	Action
P95 latency > threshold + CPU < 70%	Scale up workers
CPU utilisation > 90%	Scale down workers
Memory pressure HIGH/CRITICAL	Shed workers immediately
P95 latency very low (idle)	Scale down to save resources
CPU load changes	Auto-tune steal batch size

from pyaccelerate.adaptive import AdaptiveScheduler, AdaptiveConfig

cfg = AdaptiveConfig(
    min_workers=2,
    max_workers=16,
    cooldown_seconds=2.0,
)

with AdaptiveScheduler(config=cfg) as sched:
    results = sched.map(process, [(item,) for item in data])
    print(sched.snapshot())  # workers, p95, cpu%, mem_pressure, adjustments

Or via the Engine:

engine = Engine()
with engine.adaptive_scheduler() as sched:
    results = sched.map(heavy_fn, items)

Native Accelerators (Optional)

For maximum throughput, compile the hot-path data structures to native code:

Cython (C extension):

pip install cython
cd src/pyaccelerate/_native
python setup_cython.py build_ext --inplace

Rust (PyO3 + crossbeam-deque — same algorithm as Tokio):

cd bindings/rust/pyaccelerate_native
pip install maturin
maturin develop --release

When a native extension is installed, it's used automatically — no code changes needed. The pure-Python fallback is always available.

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())

Auto-Tuning Feedback Loop

Benchmark your hardware, persist the optimal configuration, and auto-apply it:

from pyaccelerate.autotune import auto_tune, get_or_tune, apply_profile

# Run a full tune cycle (benchmark → save to ~/.pyaccelerate/)
profile = auto_tune()
print(f"Overall score: {profile.overall_score}/100")
print(f"Optimal IO workers: {profile.optimal_io_workers}")
print(f"Optimal CPU workers: {profile.optimal_cpu_workers}")

# Load existing or re-tune if hardware changed / profile stale
profile = get_or_tune()

# Apply to running process (sets workers, priority, energy)
apply_profile()

Hardware-Safe Configuration

Prevent runaway concurrency on constrained hardware (iGPUs, LLM inference, Ollama, etc.):

from pyaccelerate.autotune import hardware_safe_limits, clamp_config

# Get hardware-derived upper bounds
limits = hardware_safe_limits()
# Discrete GPU:  {"batch_size": 32, "max_workers": N, "max_concurrent": 4}
# Integrated GPU: {"batch_size": 8, "max_workers": 1, "max_concurrent": 2}
# CPU-only:      {"batch_size": 5, "max_workers": 1, "max_concurrent": 2}

# Clamp user/config values to safe maximums
user_config = {"batch_size": 50, "max_workers": 4}
safe = clamp_config(user_config)
# iGPU → {"batch_size": 8, "max_workers": 1}

GPU Memory Stats

Query dedicated + shared VRAM and hardware details for the best GPU:

from pyaccelerate.memory import get_gpu_memory_stats

stats = get_gpu_memory_stats()
# {"gpu_available": 1.0, "gpu_dedicated_gb": 6.0,
#  "gpu_shared_gb": 28.0, "gpu_total_gb": 34.0,
#  "gpu_is_discrete": 1.0, "gpu_vulkan": 1.0,
#  "gpu_cuda_cores": 1408, "gpu_tensor_cores": 0,
#  "gpu_has_hw_encode": 1.0, "gpu_has_hw_decode": 1.0,
#  "gpu_memory_bandwidth_gbps": 336.0, "gpu_boost_clock_mhz": 2130}

GPU Shared VRAM & Vulkan Detection

The GPUDevice dataclass exposes comprehensive GPU hardware details — architecture, cores, features, clocks, memory type, driver, PCIe, and shared VRAM:

from pyaccelerate.gpu import detect_all

for g in detect_all():
    print(f"{g.name}: {g.memory_gb:.1f} GB dedicated")
    print(f"  Architecture:    {g.architecture} | CC {g.cuda_capability}")
    print(f"  Cores:           CUDA={g.cuda_cores} Tensor={g.tensor_cores} RT={g.rt_cores}")
    print(f"  Shared VRAM:     {g.shared_memory_gb:.1f} GB")
    print(f"  Total:           {g.total_memory_gb:.1f} GB")
    print(f"  Memory:          {g.memory_type} | {g.memory_bus_width}-bit | {g.memory_bandwidth_gbps:.0f} GB/s")
    print(f"  Clock:           {g.boost_clock_mhz} MHz boost")
    print(f"  Features:        {', '.join(g.features)}")
    print(f"  HW Encode/Dec:   NVENC={g.has_nvenc} NVDEC={g.has_nvdec}")
    print(f"  PCIe:            Gen{g.pcie_gen} x{g.pcie_width}")
    print(f"  Driver:          {g.driver_version} | CUDA: {g.cuda_driver_version}")
    print(f"  Vulkan:          {g.vulkan_version or 'not detected'}")

Supported GPU Attributes

Field	Type	Description
architecture	str	GPU architecture (Turing, Ampere, Ada Lovelace, RDNA 3, etc.)
cuda_capability	str	NVIDIA Compute Capability (e.g. "7.5")
cuda_cores	int	Total CUDA cores (NVIDIA) or Stream Processors (AMD)
tensor_cores	int	Tensor core count (RTX / data-center only)
rt_cores	int	RT core count (RTX only)
has_tensor	bool	Tensor / mixed-precision acceleration available
has_raytracing	bool	Hardware ray tracing available
has_nvenc	bool	Hardware video encoder (NVENC / VCN)
has_nvdec	bool	Hardware video decoder (NVDEC / VCN)
clock_mhz	int	Base clock (MHz)
boost_clock_mhz	int	Boost clock (MHz)
memory_clock_mhz	int	Memory clock (MHz)
memory_type	str	Memory type (GDDR5, GDDR6, GDDR6X, HBM2e, HBM3)
memory_bus_width	int	Memory bus width (bits)
memory_bandwidth_gbps	float	Effective memory bandwidth (GB/s)
pcie_gen	int	PCIe generation (3, 4, 5)
pcie_width	int	PCIe link width (16, 8, etc.)
driver_version	str	GPU driver version
cuda_driver_version	str	CUDA runtime version
power_limit_w	int	TDP / power limit (watts)
copy_engines	int	Async DMA copy engines
features	list[str]	Capability flags (compute, tensor, hw_encode, cuda_7.5, turing, etc.)
shared_memory_bytes	int	Shared system memory (all vendors on Windows)
vulkan_version	str	Vulkan API version

NVIDIA Architecture Coverage

Kepler (3.0) → Maxwell (5.x) → Pascal (6.x) → Volta (7.0) → Turing (7.5) → Ampere (8.x) → Ada Lovelace (8.9) → Hopper (9.0) → Blackwell (10.x)

• GTX 16xx (Turing): CUDA cores + NVENC/NVDEC, no Tensor/RT cores
• RTX 20xx (Turing): Full Tensor + RT cores
• RTX 30xx (Ampere): Enhanced Tensor + RT cores, 2nd gen
• RTX 40xx (Ada Lovelace): 4th gen Tensor, 3rd gen RT, DLSS 3
• RTX 50xx (Blackwell): 5th gen Tensor, 4th gen RT
• Data center (A100/H100/B100): Tensor cores, no RT cores

AMD Architecture Coverage

GCN 4 (RX 400/500) → GCN 5 (Vega) → RDNA (RX 5000) → RDNA 2 (RX 6000) → RDNA 3 (RX 7000) → RDNA 4 (RX 9000) → CDNA/CDNA 2/CDNA 3 (Instinct MI series)

Prometheus Metrics

Expose CPU/GPU/NPU/memory/pool metrics in Prometheus format:

from pyaccelerate.metrics import start_metrics_server, get_metrics_text

# Start /metrics endpoint on port 9090
start_metrics_server(port=9090)

# Or get text for your own framework
text = get_metrics_text()

pyaccelerate metrics --port 9090     # Start server
pyaccelerate metrics --once          # Print and exit
curl http://localhost:9090/metrics    # Scrape

HTTP / gRPC Server

Multi-language access to all PyAccelerate features:

from pyaccelerate.server import PyAccelerateServer

with PyAccelerateServer(http_port=8420, grpc_port=50051) as srv:
    print(f"HTTP: {srv.http_url}/api/v1")
    # Block until Ctrl+C
    srv.start(block=True)

pyaccelerate serve --http-port 8420 --grpc-port 50051
curl http://localhost:8420/api/v1/info    # JSON
curl http://localhost:8420/api/v1/cpu
curl http://localhost:8420/api/v1/gpu
curl http://localhost:8420/api/v1/metrics  # Prometheus text

Kubernetes Integration

Pod detection, GPU node capacity, auto-scaling recommendations & manifest generation:

from pyaccelerate.k8s import (
    is_kubernetes, get_pod_info,
    get_scaling_recommendation, generate_resource_manifest,
)

if is_kubernetes():
    pod = get_pod_info()
    print(f"Pod: {pod.name} | GPU: {pod.gpu_limit}")

rec = get_scaling_recommendation()
print(f"Replicas: {rec.recommended_replicas} ({rec.reason})")

yaml = generate_resource_manifest(name="ml-worker", gpu_per_replica=1)

pyaccelerate k8s                # Show pod & GPU info
pyaccelerate k8s --manifest     # Generate Deployment YAML
pyaccelerate k8s --json         # Machine-readable

Node.js / npm Client

A zero-dependency Node.js client is included in bindings/nodejs/:

const { PyAccelerate } = require('pyaccelerate');

const client = new PyAccelerate('http://localhost:8420');
const info = await client.getInfo();
const metrics = await client.getMetrics();
const bench = await client.runBenchmark();

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]

# gRPC server mode
pip install pyaccelerate[grpc]

# Kubernetes integration
pip install pyaccelerate[k8s]

# 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

# Run benchmarks
python -m benchmarks.run            # full suite
python -m benchmarks.run --quick    # CI-friendly (fewer tasks)
python -m benchmarks.run --io       # IO-bound only
python -m benchmarks.run --cpu      # CPU-bound only
python -m benchmarks.run --mixed    # mixed workloads only
python -m benchmarks.run --npu      # NPU inference only

# 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
├── npu/
│   ├── detector.py # NPU detection (Intel, Qualcomm, Apple)
│   ├── onnx_rt.py  # ONNX Runtime inference
│   ├── openvino.py # OpenVINO inference
│   └── inference.py# Unified inference API
├── virt.py         # Virtualization detection
├── memory.py       # Memory monitoring & buffer pool
├── profiler.py     # Timing & profiling utilities
├── benchmark.py    # Built-in micro-benchmarks
├── priority.py     # OS task priority & energy profiles
├── max_mode.py     # Maximum optimization mode
├── iot.py          # IoT / SBC hardware detection
├── autotune.py     # Auto-tuning feedback loop
├── metrics.py      # Prometheus metrics exporter
├── server.py       # HTTP + gRPC multi-language API
├── k8s.py          # Kubernetes pod & GPU integration
├── lockfree_queue.py # Lock-free MPMC & Chase-Lev deques
├── work_stealing.py  # Work-stealing scheduler (Tokio/Go/FJP)
├── adaptive.py       # Adaptive pressure-driven scheduler
├── engine.py         # Unified orchestrator
├── cli.py            # Command-line interface
├── _native/          # Optional Cython accelerators
│   ├── _fast_deque.pyx
│   └── setup_cython.py
└── bindings/
    ├── nodejs/       # npm client for Node.js / TypeScript
    └── rust/         # PyO3 Rust native extension
        └── pyaccelerate_native/

Examples

The examples/ directory contains runnable scripts demonstrating all features:

Example	Description
example_basic.py	Engine creation, summary, submit, run_parallel, batch
example_parallel_io.py	Parallel download/process/write with public UCI ML datasets
example_cpu_bound.py	Sequential vs thread pool vs process pool comparison
example_max_mode.py	MaxMode context manager, run_all, run_io, run_cpu, pipeline
example_pipeline.py	Multi-stage data pipeline (download → analyze → report)
example_priority.py	TaskPriority levels, EnergyProfile, presets, benchmarking

cd examples
python example_basic.py
python example_max_mode.py
python example_priority.py

Roadmap

• IoT / SBC detection (Raspberry Pi, Jetson, Coral, Hailo)
• Auto-tuning feedback loop (benchmark → config → re-tune)
• Prometheus metrics exporter
• gRPC server mode for multi-language integration
• Kubernetes operator for auto-scaling GPU workloads
• npm package (Node.js bindings via HTTP API)
• Work-stealing scheduler (Tokio / Go / ForkJoinPool style)
• Lock-free task queues (Chase-Lev deques, MPMC)
• Adaptive scheduler (latency, CPU & memory pressure)
• Optional native accelerators (Cython + Rust/PyO3)
• Benchmark suite with IO/CPU/mixed workload comparisons
• Shared VRAM detection for integrated GPUs (Intel Iris Xe, etc.)
• Vulkan version probing per GPU
• Hardware-safe configuration limits (hardware_safe_limits, clamp_config)
• GPU memory stats API (get_gpu_memory_stats)
• NPU detection for Intel Core Ultra (Arrow Lake / Meteor Lake) with TOPS estimation
• NPU inference benchmarks (NPU vs CPU, ONNX models)
• Interactive dependency installer in pyaccelerate info CLI

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.

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Last updated: 2026-03-27 15:46 UTC (refreshed every Monday via GitHub Actions — run manually)

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