high performance computing on graphics processing units: hgpu.org

hgpu.org » CUDA

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

Changqing Shi, Yufei Sun, Rui Chen, Jiahao Wang, Qiang Guo, Chunye Gong, Yicheng Sui, Yutong Jin, Yuzhi Zhang

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Tags: Code generation, Computer science, CUDA, HPC, Memory model, nVidia, OpenCL, Package

March 3, 2025 by hgpu

pyATF: Constraint-Based Auto-Tuning in Python

Richard Schulze, Sergei Gorlatch, Ari Rasch

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Tags: Auto-Tuning, Compilers, Computer science, CUDA, nVidia A100, OpenCL, Package, Performance, Python

March 3, 2025 by hgpu

TritonBench: Benchmarking Large Language Model Capabilities for Generating Triton Operators

Jianling Li, Shangzhan Li, Zhenye Gao, Qi Shi, Yuxuan Li, Zefan Wang, Jiacheng Huang, Haojie Wang, Jianrong Wang, Xu Han, Zhiyuan Liu, Maosong Sun

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Tags: Benchmarking, Code generation, Computer science, CUDA, Deep learning, LLM, nVidia, nVidia A100, Package, Python

March 3, 2025 by hgpu

CRIUgpu: Transparent Checkpointing of GPU-Accelerated Workloads

Radostin Stoyanov, Viktória Spišaková, Jesus Ramos, Steven Gurfinkel, Andrei Vagin, Adrian Reber, Wesley Armour, Rodrigo Bruno

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Tags: AMD Radeon Instinct MI210, ATI, Computer science, CUDA, Deep learning, nVidia, nVidia A100, nVidia H100, nVidia RTX A6000, Package, ROCm

March 3, 2025 by hgpu

Towards Studying the Effect of Compiler Optimizations and Software Randomization on GPU Reliability

Pau López Castillón, Xavier Caricchio Hernández, Leonidas Kosmidis

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Tags: Compilers, Computer science, CUDA, nVidia, nVidia GeForce GTX 1080 Ti

March 3, 2025 by hgpu

KernelBench: Can LLMs Write Efficient GPU Kernels?

Anne Ouyang, Simon Guo, Simran Arora, Alex L. Zhang, William Hu, Christopher Ré, Azalia Mirhoseini

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Tags: AI, Benchmarking, Computer science, CUDA, LLM, Machine learning, nVidia, nVidia L40s, Package, PyTorch

February 24, 2025 by hgpu

The AI CUDA Engineer: Agentic CUDA Kernel Discovery, Optimization and Composition

Robert Tjarko Lange, Aaditya Prasad, Qi Sun, Maxence Faldor, Yujin Tang, David Ha

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Tags: AI, Computer science, CUDA, LLM, nVidia, nVidia H100, Package, Performance

February 24, 2025 by hgpu

cuSZp2: A GPU Lossy Compressor with Extreme Throughput and Optimized Compression Ratio

Yafan Huang, Sheng Di, Guanpeng Li, Franck Cappello

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Tags: Compression, Computer science, CUDA, nVidia, nVidia A100, nVidia GeForce RTX 3080, nVidia GeForce RTX 3090, Package, PTX

February 16, 2025 by hgpu

Leveraging LLVM OpenMP GPU Offload Optimizations for Kokkos Applications

Rahulkumar Gayatri, Shilei Tian, Stephen Olivier, Johannes Doerfert, Eric Wright

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Tags: AMD Radeon Instinct MI250X, ATI, Computer science, CUDA, HIP, MPI, nVidia, nVidia A100, OpenMP, Package, performance portability

February 16, 2025 by hgpu

Teaching An Old Dog New Tricks: Porting Legacy Code to Heterogeneous Compute Architectures With Automated Code Translation

Nicolas Nytko, Andrew Reisner, J. David Moulton, Luke N. Olson, Matthew West

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Tags: Code generation, Computer science, CUDA, Fortran, Heterogeneous systems, MPI, nVidia, nVidia A100, OpenCL

February 16, 2025 by hgpu

Vortex: Overcoming Memory Capacity Limitations in GPU-Accelerated Large-Scale Data Analytics

Yichao Yuan, Advait Iyer, Lin Ma, Nishil Talati

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Tags: AMD Radeon Instinct MI100, ATI, Computer science, CUDA, Databases, HIP, nVidia, nVidia A40, Package

February 16, 2025 by hgpu

Ilargi: a GPU Compatible Factorized ML Model Training Framework

Wenbo Sun, Rihan Hai

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Tags: Computer science, CUDA, Linear Algebra, Machine learning, nVidia, nVidia A40, Package

February 10, 2025 by hgpu

hpcbench: A set of benchmarking utilities for biomolecular simulation tools

Engineering Supercomputing Platforms for Biomolecular Applications

HPCTransCompile: An AI Compiler Generated Dataset for High-Performance CUDA Transpilation and LLM Preliminary Exploration

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Can Large Language Models Predict Parallel Code Performance?

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high performance computing on graphics processing units: hgpu.org

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

pyATF: Constraint-Based Auto-Tuning in Python

TritonBench: Benchmarking Large Language Model Capabilities for Generating Triton Operators

CRIUgpu: Transparent Checkpointing of GPU-Accelerated Workloads

Towards Studying the Effect of Compiler Optimizations and Software Randomization on GPU Reliability

KernelBench: Can LLMs Write Efficient GPU Kernels?

The AI CUDA Engineer: Agentic CUDA Kernel Discovery, Optimization and Composition

cuSZp2: A GPU Lossy Compressor with Extreme Throughput and Optimized Compression Ratio

Leveraging LLVM OpenMP GPU Offload Optimizations for Kokkos Applications

Teaching An Old Dog New Tricks: Porting Legacy Code to Heterogeneous Compute Architectures With Automated Code Translation

Vortex: Overcoming Memory Capacity Limitations in GPU-Accelerated Large-Scale Data Analytics

Ilargi: a GPU Compatible Factorized ML Model Training Framework

Recent source codes

hpcbench: A set of benchmarking utilities for biomolecular simulation tools

HPCTransCompile: An AI Compiler Generated Dataset for High-Performance CUDA Transpilation and LLM Preliminary Exploration

chemtrain: Training Molecular Dynamics Potentials in JAX

microSYCL: SYCL micro-benchmarks repository

XaaS containers

CASS: Cuda-Amd aSSembly

Cluser of smartphones for edge computing application using TensorFlow

SYCL Container

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Can Large Language Models Predict Parallel Code Performance?

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