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CuPBoP: Making CUDA a Portable Language

Ruobing Han, Jun Chen, Bhanu Garg, Xule Zhou, John Lu, Jeffrey Young, Jaewoong Sim, Hyesoon Kim

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Tags: Computer science, CUDA, HIP, nVidia, nVidia A30, OpenCL, Package, Portability, SYCL

May 12, 2024 by hgpu

GROMACS on AMD GPU-Based HPC Platforms: Using SYCL for Performance and Portability

Andrey Alekseenko, Szilárd Páll, Erik Lindahl

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Tags: AMD Radeon Instinct MI250X, ATI, Biomolecules, Computational Physics, Computer science, Heterogeneous systems, Molecular dynamics, Molecular simulation, Performance, SYCL

May 5, 2024 by hgpu

SimSYCL: A SYCL Implementation Targeting Development, Debugging, Simulation and Conformance

Peter Thoman, Fabian Knorr, Luigi Crisci

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Tags: Computer science, HPC, Package, Performance, SYCL

April 21, 2024 by hgpu

Balancing Tracking Granularity and Parallelism in Many-Task Systems: The Horizons Approach

Peter Thoman, Philip Salzmann

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

April 14, 2024 by hgpu

Using Intel oneAPI for Multi-hybrid Acceleration Programming with GPU and FPGA Coupling

Wentao Liang, Norihisa Fujita, Ryohei Kobayashi, Taisuke Boku

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Tags: Computer science, CUDA, FPGA, Heterogeneous systems, nVidia, oneAPI, OpenCL, SYCL, Tesla V100

April 7, 2024 by hgpu

SYCL in the edge: performance and energy evaluation for heterogeneous acceleration

Youssef Faqir-Rhazoui, Carlos García

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Tags: Computer science, CUDA, nVidia, nVidia Jetson Orin Nano, Optical flow, Package, Performance, SYCL

March 18, 2024 by hgpu

SYCL-Bench 2020: Benchmarking SYCL 2020 on AMD, Intel, and NVIDIA GPUs

Luigi Crisci, Lorenzo Carpentieri, Peter Thoman, Aksel Alpay, Vincent Heuveline, Biagio Cosenza

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Tags: AMD Radeon Instinct MI100, ATI, Benchmarking, Computer science, Heterogeneous systems, HPC, Intel, Intel Ponte Vecchio Max 1100, nVidia, oneAPI, Portability, SYCL, Tesla V100

March 10, 2024 by hgpu

Assessing opportunities of SYCL for biological sequence alignment on GPU-based systems

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

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Tags: AMD Radeon RX 6700 XT, AMD Radeon RX Vega 6, ATI, Bioinformatics, Biology, Computational biology, CUDA, Heterogeneous systems, Intel Arc A770, Intel UHD 630, nVidia, nVidia GeForce GTX 1080, nVidia GeForce GTX 980, nVidia GeForce RTX 2070, nVidia GeForce RTX 3090, nVidia V100, oneAPI, Package, Sequence alignment, SYCL

February 25, 2024 by hgpu

An Evaluative Comparison of Performance Portability across GPU Programming Models

Joshua H. Davis, Pranav Sivaraman, Isaac Minn, Konstantinos Parasyris, Harshitha Menon, Giorgis Georgakoudis, Abhinav Bhatele

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Tags: AMD Radeon Instinct MI250X, AMD Radeon Instinct Mi50, ATI, Computer science, CUDA, Heterogeneous systems, HIP, MPI, nVidia, nVidia V100, OpenACC, OpenMP, Performance, performance portability, SYCL

February 18, 2024 by hgpu

Out of kernel tuning and optimizations for portable large-scale docking experiments on GPUs

Gianmarco Accordi, Davide Gadioli, Emanele Vitali, Luigi Crisci, Biagio Cosenza, Andrea Beccari, Gianluca Palermo

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Tags: Computer science, CUDA, HPC, molecular docking, nVidia, nVidia A100, oneAPI, Performance, SYCL

February 12, 2024 by hgpu

Deductive verification for SYCL

Ellen Wittingen

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Tags: Computer science, CUDA, Heterogeneous systems, HIP, nVidia, OpenCL, OpenMP, Package, SYCL, Thesis

February 4, 2024 by hgpu

Towards a GPU-Parallelization of the neXtSIM-DG Dynamical Core

Robert Jendersie, Christian Lessig, Thomas Richter

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Tags: Computer science, CUDA, Earth and Space Sciences, Finite element method, Numerical Analysis, nVidia, nVidia A100, nVidia GeForce RTX 3090, OpenMP, Package, PyTorch, SYCL

February 4, 2024 by hgpu

ParaCodex: A Profiling-Guided Autonomous Coding Agent for Reliable Parallel Code Generation and Translation

SeedFold: Scaling Biomolecular Structure Prediction

Tilus: A Tile-Level GPU Kernel Programming Language

Tilus: A Tile-Level GPGPU Programming Language for Low-Precision Computation

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

BoltzGen:Toward Universal Binder Design

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

MATLAB Tensor Core models

Accurate Models of NVIDIA Tensor Cores

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

TritonForge: Profiling-Guided Framework for Automated Triton Kernel Optimization

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

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high performance computing on graphics processing units: hgpu.org

CuPBoP: Making CUDA a Portable Language

GROMACS on AMD GPU-Based HPC Platforms: Using SYCL for Performance and Portability

SimSYCL: A SYCL Implementation Targeting Development, Debugging, Simulation and Conformance

Balancing Tracking Granularity and Parallelism in Many-Task Systems: The Horizons Approach

Using Intel oneAPI for Multi-hybrid Acceleration Programming with GPU and FPGA Coupling

SYCL in the edge: performance and energy evaluation for heterogeneous acceleration

SYCL-Bench 2020: Benchmarking SYCL 2020 on AMD, Intel, and NVIDIA GPUs

Assessing opportunities of SYCL for biological sequence alignment on GPU-based systems

An Evaluative Comparison of Performance Portability across GPU Programming Models

Out of kernel tuning and optimizations for portable large-scale docking experiments on GPUs

Deductive verification for SYCL

Towards a GPU-Parallelization of the neXtSIM-DG Dynamical Core

Recent source codes

ParaCodex: A Profiling-Guided Autonomous Coding Agent for Reliable Parallel Code Generation and Translation

SeedFold: Scaling Biomolecular Structure Prediction

Tilus: A Tile-Level GPU Kernel Programming Language

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

BoltzGen:Toward Universal Binder Design

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

MATLAB Tensor Core models

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

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