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Experience Migrating OpenCL to SYCL: A Case Study on Searches for Potential Off-Target Sites of Cas9 RNA-Guided Endonucleases on AMD GPUs

Zheming Jin, Jeffrey S. Vetter

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Tags: AMD Radeon Instinct MI100, AMD Radeon Instinct MI60, AMD Radeon VII, ATI, ATI Radeon HD 7870, Bioinformatics, Biology, OpenCL, performance portability, RNA, SYCL

October 1, 2023 by hgpu

Evaluating the performance portability of SYCL across CPUs and GPUs on bandwidth-bound applications

Istvan Z Reguly

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Tags: AMD Radeon Instinct MI250X, ATI, Benchmarking, cfd, Computer science, CUDA, Fluid dynamics, HIP, Intel, Intel Ponte Vecchio Max 1100, nVidia, nVidia A100, OpenCL, performance portability, SYCL

September 24, 2023 by hgpu

Comparing Performance and Portability between CUDA and SYCL for Protein Database Search on NVIDIA, AMD, and Intel GPUs

Manuel Costanzo, Enzo Rucci, Carlos García Sánchez, Marcelo Naiouf, Manuel Prieto-Matías

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Tags: AMD Radeon RX 6700 XT, ATI, Bioinformatics, Biology, Computer science, CUDA, Intel, Intel UHD 630, nVidia, nVidia GeForce GTX 1080, nVidia GeForce GTX 980, nVidia GeForce RTX 2070, nVidia GeForce RTX 3070, nVidia GeForce RTX 3090, oneAPI, Programming Languages, Smith-Waterman algorithm, SYCL, Tesla V100

September 24, 2023 by hgpu

Many Cores, Many Models: GPU Programming Model vs. Vendor Compatibility Overview

Andreas Herten

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Tags: Computer science, CUDA, Fortran, HIP, HPC, nVidia, Overview, programming model, SYCL

September 17, 2023 by hgpu

Porting Batched Iterative Solvers onto Intel GPUs with SYCL

Phuong Nguyen, Pratik Nayak, Hartwig Anzt

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Tags: Computer science, CUDA, Intel, Intel Data Center GPU Max 1550, nVidia, nVidia A100, nVidia H100, Package, performance portability, Physics, SYCL

August 20, 2023 by hgpu

Quantifying OpenMP: Statistical Insights into Usage and Adoption

Tal Kadosh, Niranjan Hasabnis, Timothy Mattson, Yuval Pinter, Gal Oren

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Tags: Computer science, CUDA, Databases, Fortran, HPC, MPI, nVidia, OpenACC, OpenCL, OpenMP, Package, SYCL

August 20, 2023 by hgpu

SYnergy: Fine-grained Energy-Efficient Heterogeneous Computing for Scalable Energy Saving

Kaijie Fan, Marco D'Antonio, Lorenzo Carpentieri, Biagio Cosenza, Federico Ficarelli, Daniele Cesarini

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Tags: AMD Radeon Instinct MI100, ATI, Computer science, Energy-efficient computing, GPU cluster, Heterogeneous systems, Machine learning, nVidia, nVidia V100, SYCL

August 13, 2023 by hgpu

A portable C++ library for memory and compute abstraction on multi-core CPUs and GPUs

Pietro Incardona, Aryaman Gupta, Serhii Yaskovets, Ivo F. Sbalzarini

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Tags: AMD RX Vega 64, ATI, Computer science, CUDA, nVidia, nVidia A100, nVidia GeForce RTX 3090, OpenACC, OpenCL, OpenMP, Package, Performance, performance portability, SYCL

July 30, 2023 by hgpu

SYCL compute kernels for ExaHyPE

Chung Ming Loi, Tobias Weinzierl

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Tags: Benchmarking, Computer science, Data parallelism, nVidia, nVidia A100, Package, Programming techniques, SYCL

July 2, 2023 by hgpu

Hybrid CPU/GPU/APU accelerated query, insert, update and erase operations in hash tables with string keys

Tobias Groth, Sven Groppe, Thilo Pionteck, Franz Valdiek, Martin Koppehel

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Tags: APU, Computer science, CUDA, Hashing, nVidia A100, oneAPI, SYCL

June 4, 2023 by hgpu

Experiences Migrating CUDA to SYCL: A Molecular Docking Case Study

Leonardo Solis-Vasquez, Edward Mascarenhas, Andreas Koch

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Tags: Chemistry, Computer science, CUDA, molecular docking, nVidia, nVidia A100, oneAPI, Package, SYCL

May 28, 2023 by hgpu

An Asynchronous Dataflow-Driven Execution Model For Distributed Accelerator Computing

Philip Salzmann, Fabian Knorr, Peter Thoman, Philipp Gschwandtner, Biagio Cosenza, Thomas Fahringer

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Tags: Benchmarking, Computer science, CUDA, GPU cluster, MPI, nVidia, nVidia V100, Package, SYCL

May 21, 2023 by hgpu

Agentic Code Optimization via Compiler-LLM Cooperation

MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU

Device Virtual Machine (DVM)

DVM: Real-Time Kernel Generation for Dynamic AI Models

AutoKernel: Autoresearch for GPU kernels. Give it any PyTorch model, go to sleep, wake up to optimized Triton kernels

AutoKernel: Autonomous GPU Kernel Optimization via Iterative Agent-Driven Search

SOL-ExecBench: Speed-of-Light Benchmarking for Real-World GPU Kernels Against Hardware Limits

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context

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high performance computing on graphics processing units: hgpu.org

Experience Migrating OpenCL to SYCL: A Case Study on Searches for Potential Off-Target Sites of Cas9 RNA-Guided Endonucleases on AMD GPUs

Evaluating the performance portability of SYCL across CPUs and GPUs on bandwidth-bound applications

Comparing Performance and Portability between CUDA and SYCL for Protein Database Search on NVIDIA, AMD, and Intel GPUs

Many Cores, Many Models: GPU Programming Model vs. Vendor Compatibility Overview

Porting Batched Iterative Solvers onto Intel GPUs with SYCL

Quantifying OpenMP: Statistical Insights into Usage and Adoption

SYnergy: Fine-grained Energy-Efficient Heterogeneous Computing for Scalable Energy Saving

A portable C++ library for memory and compute abstraction on multi-core CPUs and GPUs

SYCL compute kernels for ExaHyPE

Hybrid CPU/GPU/APU accelerated query, insert, update and erase operations in hash tables with string keys

Experiences Migrating CUDA to SYCL: A Molecular Docking Case Study

An Asynchronous Dataflow-Driven Execution Model For Distributed Accelerator Computing

Recent source codes

Agentic Code Optimization via Compiler-LLM Cooperation

MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU

Device Virtual Machine (DVM)

AutoKernel: Autoresearch for GPU kernels. Give it any PyTorch model, go to sleep, wake up to optimized Triton kernels

SOL-ExecBench: Speed-of-Light Benchmarking for Real-World GPU Kernels Against Hardware Limits

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context

LLM.Q: Quantized LLM training in pure CUDA/C++

True 4-Bit Quantized CNN Training on CPU

cuFuzz: A GPU-oriented coverage-guided fuzzer for userland CUDA application

KernelSkill: A Multi-Agent Framework for GPU Kernel Optimization

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