high performance computing on graphics processing units: hgpu.org

Chris Cummins, Bram Wasti, Jiadong Guo, Brandon Cui, Jason Ansel, Sahir Gomez, Somya Jain, Jia Liu, Olivier Teytaud, Benoit Steiner, Yuandong Tian, Hugh Leather

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Tags: Artificial intelligence, Benchmarking, Compilers, Computer science, CUDA, Machine learning, nVidia, Package, Performance

September 26, 2021 by hgpu

GRIM: A General, Real-Time Deep Learning Inference Framework for Mobile Devices based on Fine-Grained Structured Weight Sparsity

Wei Niu, Zhengang Li, Xiaolong Ma, Peiyan Dong, Gang Zhou, Xuehai Qian, Xue Lin, Yanzhi Wang, Bin Ren

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Tags: Artificial intelligence, Code generation, Computer science, Deep learning, DSL, Neural networks, nVidia, nVidia Titan RTX, OpenCL

August 29, 2021 by hgpu

GPTPU: Accelerating Applications using Edge Tensor Processing Units

Kuan-Chieh Hsu, Hung-Wei Tseng

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Tags: Algorithms, Artificial intelligence, Computer science, CUDA, Machine learning, Neural networks, nVidia, OpenCL, TPU

July 18, 2021 by hgpu

Optimization of Heterogeneous Systems with AI Planning Heuristics and Machine Learning: A Performance and Energy Aware Approach

Suejb Memeti, Sabri Pllana

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Tags: Artificial intelligence, Computer science, Heterogeneous systems, Intel Xeon Phi, Machine learning

June 6, 2021 by hgpu

TransCL: An Automatic CUDA-to-OpenCL Programs Transformation Framework

pyATF: The Auto-Tuning Framework (ATF) in Python

pyATF: Constraint-Based Auto-Tuning in Python

TritonBench: Benchmarking Large Language Model Capabilities for Generating Triton Operators

Checkpoint/Restore tool

CRIUgpu: Transparent Checkpointing of GPU-Accelerated Workloads

KernelBench: Can LLMs Write GPU Kernels?

KernelBench: Can LLMs Write Efficient GPU Kernels?

An MLIR-based toolchain for AMD AI Engine-enabled devices

Seamless acceleration of Fortran intrinsics via AMD AI engines

Forecasting time series with constraints

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high performance computing on graphics processing units: hgpu.org

The Open MatSci ML Toolkit: A Flexible Framework for Machine Learning in Materials Science

Benchmarking GPU and TPU Performance with Graph Neural Networks

SOL: Reducing the Maintenance Overhead for Integrating Hardware Support into AI Frameworks

The Application of AI Technology in GPU Scheduling Algorithm Optimization

The Ecological Footprint of Neural Machine Translation Systems

Beyond Desktop Computation: Challenges in Scaling a GPU Infrastructure

Artificial Intelligence in Electric Machine Drives: Advances and Trends

CompilerGym: Robust, Performant Compiler Optimization Environments for AI Research

GRIM: A General, Real-Time Deep Learning Inference Framework for Mobile Devices based on Fine-Grained Structured Weight Sparsity

GPTPU: Accelerating Applications using Edge Tensor Processing Units

Optimization of Heterogeneous Systems with AI Planning Heuristics and Machine Learning: A Performance and Energy Aware Approach

Recent source codes

wgpy: WebGL accelerated numpy-compatible array library for web browser

Microbenchmarking OpenMP target offload with Catch2

SUperman: Highly Efficient Permanent Computation Library

TransCL: An Automatic CUDA-to-OpenCL Programs Transformation Framework

pyATF: The Auto-Tuning Framework (ATF) in Python

TritonBench: Benchmarking Large Language Model Capabilities for Generating Triton Operators

Checkpoint/Restore tool

KernelBench: Can LLMs Write GPU Kernels?

An MLIR-based toolchain for AMD AI Engine-enabled devices

Forecasting time series with constraints

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