high performance computing on graphics processing units: hgpu.org

Programming

OpenMP Kernel Language Extensions for Performance Portable GPU Codes

Shilei Tian, Tom Scogland, Barbara Chapman, Johannes Doerfert

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Tags: AMD Radeon Instinct MI250, ATI, Benchmarking, Compilers, Computer science, CUDA, HIP, nVidia, nVidia A100, OpenMP, performance portability

October 1, 2023 by hgpu

Novel Parallelization Strategies for High-Performance DNN Training on HPC Systems

Arpan Jain

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Tags: Artificial intelligence, Computer science, CUDA, Data parallelism, Deep learning, HPC, Machine learning, MPI, Neural networks, nVidia, Speech recognition, Tesla K80, Tesla V100, Thesis

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

Comparing Llama-2 and GPT-3 LLMs for HPC kernels generation

Pedro Valero-Lara, Alexis Huante, Mustafa Al Lail, William F. Godoy, Keita Teranishi, Prasanna Balaprakash, Jeffrey S. Vetter

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Tags: Code generation, Computer science, CUDA, Deep learning, Fortran, HIP, HPC, nVidia, OpenACC, OpenMP, Package, Python

September 17, 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

host device — Generic programming in Cuda

Thomas Mejstrik

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Tags: Computer science, CUDA, nVidia, Programming Languages

September 17, 2023 by hgpu

Unified Shared Memory: Friend or Foe?

Juan Fumero Alfonso, Florin-Gabriel Blanaru, Athanasios Stratikopoulos, Steve Dohrmann, Sandhya Viswanathan, Christos-Efthymios Kotselidis

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Tags: Code generation, Computer science, CUDA, FPGA, Heterogeneous systems, Java, Memory, nVidia, nVidia GeForce RTX 3070, OpenCL

September 17, 2023 by hgpu

Leveraging Memory Copy Overlap for Efficient Sparse Matrix-Vector Multiplication on GPUs

Guangsen Zeng, Yi Zou

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Tags: Computer science, CUDA, Machine learning, nVidia, nVidia A40, Sparse matrix

September 6, 2023 by hgpu

Sieve: Stratified GPU-Compute Workload Sampling

Mahmood Naderan-Tahan, Hossein SeyyedAghaei, Lieven Eeckhout

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Tags: Computer science, CUDA, nVidia, nVidia GeForce RTX 2080 Ti, nVidia GeForce RTX 3080, Package, Performance

August 28, 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

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

gpu_tracker: Python package for tracking and profiling GPU utilization in both desktop and high-performance computing environments

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

LOOPer: A Learned Automatic Code Optimizer For Polyhedral Compilers

OpenMC Monte Carlo Code

Performance Portable Monte Carlo Particle Transport on Intel, NVIDIA, and AMD GPUs

Polygeist: C/C++ frontend for MLIR

Retargeting and Respecializing GPU Workloads for Performance Portability

Parallel Gaussian process with kernel approximation in CUDA

Optical flow algorithms for SYCL

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

OpenMP5-Offload-OpenMC-Intel-PVC

Distributed OpenMP Offloading of OpenMC on Intel GPU MAX Accelerators

See all packages

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high performance computing on graphics processing units: hgpu.org

Programming

OpenMP Kernel Language Extensions for Performance Portable GPU Codes

Novel Parallelization Strategies for High-Performance DNN Training on HPC Systems

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

Comparing Llama-2 and GPT-3 LLMs for HPC kernels generation

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

host device — Generic programming in Cuda

Unified Shared Memory: Friend or Foe?

Leveraging Memory Copy Overlap for Efficient Sparse Matrix-Vector Multiplication on GPUs

Sieve: Stratified GPU-Compute Workload Sampling

Porting Batched Iterative Solvers onto Intel GPUs with SYCL

Quantifying OpenMP: Statistical Insights into Usage and Adoption

Recent source codes

QArray

Celerity: High-level C++ for Accelerator Clusters

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

OpenMC Monte Carlo Code

Polygeist: C/C++ frontend for MLIR

Parallel Gaussian process with kernel approximation in CUDA

Optical flow algorithms for SYCL

OpenMP5-Offload-OpenMC-Intel-PVC

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