PyGPUkit 0.2.0

A lightweight GPU runtime for Python with Rust-powered scheduler, NVRTC JIT compilation, and NumPy-like API

PyGPUkit — Lightweight GPU Runtime for Python

A minimal, modular GPU runtime with Rust-powered scheduler, NVRTC JIT compilation, and a clean NumPy-like API.

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Overview

PyGPUkit is a lightweight GPU runtime for Python that provides:

• Rust-powered scheduler with admission control, QoS, and resource partitioning
• NVRTC-based JIT kernel compilation
• A NumPy-like GPUArray type
• Kubernetes-inspired GPU scheduling (bandwidth + memory guarantees)
• Extensible operator set (add/mul/matmul, custom kernels)
• Minimal dependencies and embeddable runtime

PyGPUkit aims to be the "micro-runtime for GPU computing": small, fast, and ideal for research, inference tooling, DSP, and real-time systems.

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v0.2 Features (NEW)

Core Infrastructure (Rust)

Feature	Description
Memory Pool	LRU eviction, size-class free lists
Scheduler	Priority queue, memory reservation
Transfer Engine	Separate H2D/D2H streams, priority
Kernel Dispatch	Per-stream limits, lifecycle tracking

Advanced Features (Rust)

Feature	Description
Admission Control	Deterministic admission, quota enforcement
QoS Policy	Guaranteed/Burstable/BestEffort tiers
Kernel Pacing	Bandwidth-based throttling per stream
Micro-Slicing	Kernel splitting, round-robin fairness
Pinned Memory	Page-locked host memory with pooling
Kernel Cache	PTX caching, LRU eviction, TTL
GPU Partitioning	Resource isolation, multi-tenant support
Tiled Matmul	Shared memory + double buffering

Performance (RTX 3090 Ti)

Matrix Size	Performance	vs NumPy
512x512	1262 GFLOPS	11.6x
1024x1024	1350 GFLOPS	2.2x
2048x2048	4417 GFLOPS	6.1x
4096x4096	6555 GFLOPS	7.9x

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Features

• Lightweight — no PyTorch/CuPy overhead
• Modular — runtime / memory / scheduler / JIT / ops
• Rust Backend — memory pool, scheduler, dispatch in Rust
• GPUArray with NumPy interop
• NVRTC JIT for CUDA kernels
• Advanced Scheduler with memory & bandwidth guarantees
• 106 Rust tests for core components

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Installation

pip install pygpukit

From source:

git clone https://github.com/m96-chan/PyGPUkit
cd PyGPUkit
pip install -e .

Requirements:

• Python 3.10+
• CUDA 11+
• NVRTC available
• NVIDIA GPU

Supported GPUs:

• RTX 30XX series (Ampere) and above
• Performance tuning is optimized for GPUs with large L2 cache (6MB+)
• Older GPUs (RTX 20XX, GTX 10XX, etc.) are NOT tuned and may have suboptimal performance

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Project Goals

1. Provide the smallest usable GPU runtime for Python
2. Expose GPU scheduling (bandwidth, memory, partitioning)
3. Make writing custom GPU kernels easy
4. Serve as a building block for inference engines, DSP systems, and real-time workloads

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Usage Examples

Allocate Arrays

import pygpukit as gp

x = gp.zeros((1024, 1024), dtype="float32")
y = gp.ones((1024, 1024), dtype="float32")

Basic Operations

z = gp.add(x, y)
w = gp.matmul(x, y)

CPU <-> GPU Transfer

arr = z.to_numpy()
garr = gp.from_numpy(arr)

Custom NVRTC Kernel

extern "C" __global__
void scale(float* x, float factor, int n) {
    int idx = blockIdx.x * blockDim.x + threadIdx.x;
    if (idx < n) x[idx] *= factor;
}

kernel = gp.jit(src, func="scale")
kernel(x, factor=0.5, n=x.size)

Rust Scheduler (v0.2)

import _pygpukit_rust as rust

# Memory Pool with LRU eviction
pool = rust.MemoryPool(quota=100 * 1024 * 1024, enable_eviction=True)
block = pool.allocate(4096)

# QoS-aware task scheduling
evaluator = rust.QosPolicyEvaluator(total_memory=8*1024**3, total_bandwidth=1.0)
task = rust.QosTaskMeta.guaranteed("task-1", "Critical Task", 256*1024*1024)
result = evaluator.evaluate(task)

# GPU Partitioning
manager = rust.PartitionManager(rust.PartitionConfig(total_memory=8*1024**3))
manager.create_partition("inference", "Inference",
    rust.PartitionLimits().memory(4*1024**3).compute(0.5))

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Scheduler — Kubernetes-Inspired GPU Orchestration

PyGPUkit includes an experimental scheduler that treats a single GPU as a multi-tenant compute node, similar to how Kubernetes orchestrates CPU workloads. The goal is to provide resource isolation, guarantees, and fair sharing across multiple GPU tasks.

Core Capabilities

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1. GPU Memory Reservation

Tasks may request a guaranteed block of GPU memory.

• Hard guarantees -> task is rejected if memory cannot be allocated
• Soft guarantees -> best-effort allocation
• Overcommit strategies (evict to host when pressure is high)
• Reclaim policies (LRU GPUArray eviction)

Example:

task = scheduler.submit(
    fn,
    memory="512MB",
)

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2. GPU Bandwidth Guarantees / Throttling

Tasks may request a specific percentage of GPU compute bandwidth.

Bandwidth control is implemented via:

• Stream priority
• Kernel pacing (launch intervals)
• Micro-slicing large kernels
• Cooperative time-quantized scheduling
• Persistent dispatcher kernels (planned)

Example:

task = scheduler.submit(
    fn,
    bandwidth=0.20,   # 20% GPU compute share
)

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3. Logical GPU Partitioning

PyGPUkit implements software-defined GPU slicing, similar in spirit to Kubernetes device plugin resource partitioning.

Slices may define:

• Memory quota
• Bandwidth share
• Stream priority band
• Isolation level

Useful for:

• Multi-tenant inference servers
• Real-time audio/DSP workloads
• Background/foreground GPU task separation

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4. Scheduling Policies

The scheduler supports multiple policies:

• Guaranteed — exclusive reservation, strict QoS
• Burstable — partial guarantees, opportunistic bandwidth
• BestEffort — uses leftover GPU cycles
• Priority scheduling
• Deadline scheduling (planned)
• Weighted fair sharing

Example:

task = scheduler.submit(
    fn,
    policy="guaranteed",
    memory="1GB",
    bandwidth=0.10,
)

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5. Admission Control

Before executing a task, the scheduler performs:

• Resource validation
• Quota check
• QoS matching
• Scheduling feasibility

Results in:

• admitted
• queued
• rejected

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6. Monitoring & Introspection

PyGPUkit exposes live metrics:

• Memory usage per task
• SM occupancy and GPU utilization
• Throttling / pacing logs
• Queue position / execution state
• Reclaim/eviction count

Example:

stats = scheduler.stats(task_id)

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7. Soft Isolation Model

While not OS-level isolation, each GPU task is provided:

• Dedicated stream groups
• Guaranteed memory pools
• Kernel pacing to enforce bandwidth
• Optional sandboxed GPUArray region

This provides practical multi-tenant safety without MIG/MPS.

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Project Structure

PyGPUkit/
  src/pygpukit/    # Python API (NumPy-compatible)
  native/          # C++ backend (CUDA Driver/Runtime/NVRTC)
  rust/            # Rust backend (memory pool, scheduler, dispatch)
    pygpukit-core/   # Pure Rust core logic
    pygpukit-python/ # PyO3 bindings
  examples/        # Demo scripts
  tests/           # Test suite

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Roadmap

v0.1 (Released)

• GPUArray
• NVRTC JIT
• add/mul/matmul ops
• Basic stream manager
• Packaging + wheels

v0.2 (Released)

• Rust Memory Pool (LRU, size-class)
• Rust Scheduler (priority, memory reservation)
• Rust Transfer Engine (async H2D/D2H)
• Rust Kernel Dispatch Controller
• Admission Control
• QoS Policy Framework (Guaranteed/Burstable/BestEffort)
• Kernel Pacing Engine
• Micro-Slicing Framework
• Pinned Memory Support
• Kernel Cache (PTX caching)
• GPU Partitioning
• Tiled Matmul (shared memory)
• 106 Rust tests

v0.3 (Planned)

• Triton optional backend
• Advanced ops (softmax, layernorm)
• Inference-oriented plugin system
• MPS/MIG integration

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Contributing

Contributions and discussions are welcome! Please open Issues for feature requests, bugs, or design proposals.

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License

MIT License

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Acknowledgements

Inspired by:

• CUDA Runtime
• NVRTC
• PyCUDA
• CuPy
• Triton

PyGPUkit aims to fill the gap for a tiny, embeddable GPU runtime for Python.