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Concurrent Scheduling of High-Level Parallel Programs on Multi-GPU Systems

Fabian Knorr, Philip Salzmann, Peter Thoman, Thomas Fahringer

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Tags: Benchmarking, Computer science, HPC, nVidia, nVidia A100, Package, performance portability, SYCL, Task scheduling

March 23, 2025 by hgpu

A Microbenchmark Framework for Performance Evaluation of OpenMP Target Offloading

Mohammad Atif, Tianle Wang, Zhihua Dong, Charles Leggett, Meifeng Lin

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Tags: Benchmarking, Computer science, Heterogeneous systems, nVidia A100, nVidia RTX A6000, nVidia V100, OpenMP, Package

March 10, 2025 by hgpu

SUperman: Efficient Permanent Computation on GPUs

Deniz Elbek, Fatih Taşyaran, Bora Uçar, Kamer Kaya

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Tags: Computer science, CUDA, HPC, Numerical Analysis, nVidia, nVidia A100, nVidia Quadro GV100, OpenMPI, Package

March 10, 2025 by hgpu

Can Tensor Cores Benefit Memory-Bound Kernels? (No!)

Lingqi Zhang, Jiajun Huang, Sheng Di, Satoshi Matsuoka, Mohamed Wahib

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Tags: Computer science, CUDA, nVidia, nVidia A100, Performance

March 10, 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

Evaluating the Performance of the DeepSeek Model in Confidential Computing Environment

Ben Dong, Qian Wang

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Tags: Benchmarking, Cloud, Computer science, LLM, nVidia, nVidia A100, Performance, Security

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

On the Partitioning of GPU Power among Multi-Instances

Tirth Vamja, Kaustabha Ray, Felix George, UmaMaheswari C Devi

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Tags: Computer science, CUDA, Energy-efficient computing, Machine learning, Matrix multiplication, nVidia, nVidia A100, nVidia V100, Performance

February 3, 2025 by hgpu

CL4SE: A Context Learning Benchmark For Software Engineering Tasks

CodeScaler: Scaling Code LLM Training and Test-Time Inference via Execution-Free Reward Models

A Safety Report on GPT-5.2, Gemini 3 Pro, Qwen3-VL, Grok 4.1 Fast, Nano Banana Pro, and Seedream 4.5

DICE: Diffusion Large Language Models Excel at Generating CUDA Kernels

KernelGYM & Dr. Kernel: A distributed GPU environment and a collection of RL training methods to support RL for Kernel Generations

Dr. Kernel: Reinforcement Learning Done Right for Triton Kernel Generations

Theorizer: from the paper Generating Literature-Driven Scientific Discoveries at Scale

Generating Literature-Driven Scientific Theories at Scale

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high performance computing on graphics processing units: hgpu.org

Concurrent Scheduling of High-Level Parallel Programs on Multi-GPU Systems

A Microbenchmark Framework for Performance Evaluation of OpenMP Target Offloading

SUperman: Efficient Permanent Computation on GPUs

Can Tensor Cores Benefit Memory-Bound Kernels? (No!)

pyATF: Constraint-Based Auto-Tuning in Python

TritonBench: Benchmarking Large Language Model Capabilities for Generating Triton Operators

CRIUgpu: Transparent Checkpointing of GPU-Accelerated Workloads

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

On the Partitioning of GPU Power among Multi-Instances

Recent source codes

CL4SE: A Context Learning Benchmark For Software Engineering Tasks

CodeScaler: Scaling Code LLM Training and Test-Time Inference via Execution-Free Reward Models

A Safety Report on GPT-5.2, Gemini 3 Pro, Qwen3-VL, Grok 4.1 Fast, Nano Banana Pro, and Seedream 4.5

DICE: Diffusion Large Language Models Excel at Generating CUDA Kernels

KernelGYM & Dr. Kernel: A distributed GPU environment and a collection of RL training methods to support RL for Kernel Generations

Vortex-Optimized Light-weight Toolchain (VOLT)

SciDef: Automated Definition Extraction from Scientific Literature

bioagent-bench: Benchmark for evaluating LLM agents in bioinformatics

Benchmark suite for LLM inference on NVIDIA consumer GPUs

Theorizer: from the paper Generating Literature-Driven Scientific Discoveries at Scale

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