high performance computing on graphics processing units: hgpu.org

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Scalable GPU-Based Integrity Verification for Large Machine Learning Models

Marcin Spoczynski, Marcela S. Melara

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Tags: Computer science, Machine learning, nVidia, oneAPI, Package, PyTorch, Rust, Security, SYCL

November 2, 2025 by hgpu

Adaptivity in AdaptiveCpp: Optimizing Performance by Leveraging Runtime Information During JIT-Compilation

Aksel Alpay, Vincent Heuveline

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Tags: AMD Radeon Pro VII, ATI, Benchmarking, Computer science, CUDA, HIP, Intel UHD 630, nVidia, nVidia RTX A5000, Package, Performance, SYCL

October 19, 2025 by hgpu

An HPC Benchmark Survey and Taxonomy for Characterization

Andreas Herten, Olga Pearce, Filipe S. M. Guimarães

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Tags: Benchmarking, Computer science, CUDA, Fortran, HIP, HPC, MPI, OpenACC, OpenCL, OpenMP, Package, Performance, ROCm, SYCL

September 14, 2025 by hgpu

Performant Unified GPU Kernels for Portable Singular Value Computation Across Hardware and Precision

Evelyne Ringoot, Rabab Alomairy, Valentin Churavy, Alan Edelman

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Tags: AMD Radeon Instinct MI250, Apple M1 Pro, ATI, Computer science, HIP, Intel, Intel Ponte Vecchio Max 1100, Kokkos, Linear Algebra, Machine learning, nVidia, nVidia A100, nVidia GeForce RTX 4060, nVidia H100, OpenCL, SYCL

August 17, 2025 by hgpu

Exploring SYCL as a Portability Layer for High-Performance Computing on CPUs

Hari Abram, Nikela Papadopoulou, Miquel Pericas

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Tags: Benchmarking, Computer science, Heterogeneous systems, oneAPI, OpenCL, OpenMP, Package, SYCL

June 8, 2025 by hgpu

Exploring SYCL for batched kernels with memory allocations

Aymeric Millan, Thomas Padioleau, Julien Bigot

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Tags: AMD Radeon Instinct MI250X, ATI, Computer science, CUDA, FFT, Neural networks, nVidia, nVidia A100, Package, performance portability, SYCL

May 25, 2025 by hgpu

Dynamic Memory Management on GPUs with SYCL

Russell K. Standish

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Tags: Computer science, CUDA, HIP, Memory model, nVidia, Package, Performance, SYCL

May 4, 2025 by hgpu

The Shamrock code: I- Smoothed Particle Hydrodynamics on GPUs

Timothée David--Cléris, Guillaume Laibe, Yona Lapeyre

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Tags: AMD, AMD Radeon Instinct MI250X, Astrophysics, CUDA, MPI, nVidia, nVidia A100, OpenMP, Package, Physics, PTX, ROCm, SYCL

March 23, 2025 by hgpu

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

ML-Triton, A Multi-Level Compilation and Language Extension to Triton GPU Programming

Dewei Wang, Wei Zhu, Liyang Ling, Ettore Tiotto, Quintin Wang, Whitney Tsang, Julian Opperman, Jacky Deng

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Tags: AI, Code generation, Compilers, Computer science, CUDA, Intel Data Center GPU Max 1550, nVidia, oneAPI, SYCL

March 23, 2025 by hgpu

CPU-GPU co-execution through the exploitation of hybrid technologies via SYCL

Nozal Raúl, Jose Luis Bosque

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Tags: Computer science, CUDA, Heterogeneous systems, Hybrid computing, LLVM, load balancing, nVidia, nVidia GeForce GT 1030, oneAPI, OpenCL, performance portability, SYCL

February 3, 2025 by hgpu

Exploring data flow design and vectorization with oneAPI for streaming applications on CPU+GPU

Cristian Campos, Rafael Asenjo, Angeles Navarro

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Tags: Computer science, Data parallelism, Heterogeneous systems, Intel, Intel UHD 770, oneAPI, Package, SYCL

January 27, 2025 by hgpu

tritonBLAS: A Lightweight Triton-based General Matrix Multiplication (GEMM) Library

tritonBLAS: Triton-based Analytical Approach for GEMM Kernel Parameter Selection

hls4ml: Machine learning on FPGAs using HLS

hls4ml: A Flexible, Open-Source Platform for Deep Learning Acceleration on Reconfigurable Hardware

ThunderKittens: Tile primitives for speedy kernels

ParallelKittens: Systematic and Practical Simplification of Multi-GPU AI Kernels

NVIDIA Nemotron Parse 1.1

Iris: AMD RAD's multi-GPU Triton-based framework for seamless multi-GPU programming

Iris: First-Class Multi-GPU Programming Experience in Triton

HipKittens: Fast and Furious AMD Kernels

CudaForge: An Agent Framework with Hardware Feedback for CUDA Kernel Optimization

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high performance computing on graphics processing units: hgpu.org

Scalable GPU-Based Integrity Verification for Large Machine Learning Models

Adaptivity in AdaptiveCpp: Optimizing Performance by Leveraging Runtime Information During JIT-Compilation

An HPC Benchmark Survey and Taxonomy for Characterization

Performant Unified GPU Kernels for Portable Singular Value Computation Across Hardware and Precision

Exploring SYCL as a Portability Layer for High-Performance Computing on CPUs

Exploring SYCL for batched kernels with memory allocations

Dynamic Memory Management on GPUs with SYCL

The Shamrock code: I- Smoothed Particle Hydrodynamics on GPUs

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

ML-Triton, A Multi-Level Compilation and Language Extension to Triton GPU Programming

CPU-GPU co-execution through the exploitation of hybrid technologies via SYCL

Exploring data flow design and vectorization with oneAPI for streaming applications on CPU+GPU

Recent source codes

tritonBLAS: A Lightweight Triton-based General Matrix Multiplication (GEMM) Library

hls4ml: Machine learning on FPGAs using HLS

ThunderKittens: Tile primitives for speedy kernels

NVIDIA Nemotron Parse 1.1

Iris: AMD RAD's multi-GPU Triton-based framework for seamless multi-GPU programming

HipKittens: Fast and Furious AMD Kernels

Fortran xDSL dialects

mt4g: Memory Topology 4 GPUs

Falcon: GPU-Based Floating-point Adaptive Lossless Compression

CudaForge: An Agent Framework with Hardware Feedback for CUDA Kernel Optimization

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