high performance computing on graphics processing units: hgpu.org

Programming

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CuPBoP-AMD: Extending CUDA to AMD Platforms

Jun Chen, Xule Zhou, Hyesoon Kim

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Tags: AMD Radeon Instinct MI210, ATI, Code generation, Computer science, CUDA, HIP, nVidia, OpenCL, Package

December 3, 2023 by hgpu

RDMA-Based Algorithms for Sparse Matrix Multiplication on GPUs

Benjamin Brock, Aydın Buluç, Katherine Yelick

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Tags: Algorithms, Computer science, CUDA, Matrix multiplication, MPI, nVidia, nVidia DGX-2, Sparse matrix, Tesla V100

December 3, 2023 by hgpu

GT4Py: High Performance Stencils for Weather and Climate Applications using Python

Enrique G. Paredes, Linus Groner, Stefano Ubbiali, Hannes Vogt, Alberto Madonna, Kean Mariotti, Felipe Cruz, Lucas Benedicic, Mauro Bianco, Joost VandeVondele, Thomas C. Schulthess

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Tags: CUDA, Earth and Space Sciences, nVidia, Package, performance portability, Python, Stencil computation, Tesla P100, Weather prediction

November 27, 2023 by hgpu

Accelerating bioinformatics applications on CUDA-enabled multi-GPU systems

Robin Kobus

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Tags: Bioinformatics, Biology, CUDA, Databases, Hashing, nVidia, nVidia Quadro GV100, Package, Sequence alignment, Tesla P100, Tesla V100, Thesis

November 27, 2023 by hgpu

Evaluation of FPGA-based high performance computing platforms

Martin Frick-Lundgren

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Tags: Computer science, FPGA, HPC, Intel, Intel UHD 630, Matrix multiplication, OpenCL, Package, SYCL, Thesis

November 27, 2023 by hgpu

FIKIT: Priority-Based Real-time GPU Multi-tasking Scheduling with Kernel Identification

Wenqing Wu

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Tags: Computer science, CUDA, nVidia, nVidia A100, nVidia GeForce RTX 3090, nVidia GeForce RTX 4080 Ti, nVidia H100, Task scheduling

November 27, 2023 by hgpu

Creating a Dataset for High-Performance Computing Code Translation using LLMs: A Bridge Between OpenMP Fortran and C+

Bin Lei, Caiwen Ding, Le Chen, Pei-Hung Lin, Chunhua Liao

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Tags: Benchmarking, Computer science, CUDA, Fortran, Machine learning, nVidia, nVidia RTX A6000, OpenMP, Package

November 19, 2023 by hgpu

GPU Auto-tuning Framework for Optimal Performance and Power Consumption

Sunbal Cheema, Gul N. Khan

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Tags: Computer science, Energy-efficient computing, OpenCL

November 19, 2023 by hgpu

CHARM-SYCL: New Unified Programming Environment for Multiple Accelerator Types

Norihisa Fujita, Beau Johnston, Ryohei Kobayashi, Keita Teranishi, Seyong Lee, Taisuke Boku, Jeffrey S. Vetter

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Tags: AMD Radeon Instinct MI100, ATI, Computer science, CUDA, Heterogeneous systems, nVidia, Package, performance portability, SYCL, Tesla V100

November 19, 2023 by hgpu

ExaNBody: a HPC framework for N-Body applications

Thierry Carrard, Raphaël Prat, Guillaume Latu, Killian Babilotte, Paul Lafourcade, Lhassan Amarsid, Laurent Soulard

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Tags: Astrophysics, Computer science, CUDA, MPI, N-body simulation, nVidia, nVidia A100, OpenMP, performance portability

November 19, 2023 by hgpu

AFOCL: Portable OpenCL Programming of FPGAs via Automated Built-in Kernel Management

Topi Leppanen, Joonas Multanen, Leevi Leppänen, Pekka Jääskeläinen

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Tags: Computer science, FPGA, OpenCL, Package, performance portability

November 19, 2023 by hgpu

On the Three P’s of Parallel Programming for Heterogeneous Computing: Performance, Productivity, and Portability

Atharva Gondhalekar, Wu-chun Feng

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Tags: Computer science, FPGA, Heterogeneous systems, HLS, nVidia, nVidia GeForce RTX 3090, oneAPI, OpenCL, Performance, performance portability, SYCL

November 12, 2023 by hgpu

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

Profile Util library: A quick and easy way to get MPI, OpenMP and GPU runtime information

MaC: Mamba CPU performance predictor

MambaCPU: Enhanced Correlation Mining with State Space Models for CPU Performance Prediction

Mixed-precision finite element kernels

Mixed-precision finite element kernels and assembly: Rounding error analysis and hardware acceleration

SHMT: The Simultaneous and Heterogenous Multithreading project

General-Purpose Computing on Tensor Processors

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

Online Energy Optimization in GPUs: A Multi-Armed Bandit Approach

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high performance computing on graphics processing units: hgpu.org

Programming

CuPBoP-AMD: Extending CUDA to AMD Platforms

RDMA-Based Algorithms for Sparse Matrix Multiplication on GPUs

GT4Py: High Performance Stencils for Weather and Climate Applications using Python

Accelerating bioinformatics applications on CUDA-enabled multi-GPU systems

Evaluation of FPGA-based high performance computing platforms

FIKIT: Priority-Based Real-time GPU Multi-tasking Scheduling with Kernel Identification

Creating a Dataset for High-Performance Computing Code Translation using LLMs: A Bridge Between OpenMP Fortran and C+

GPU Auto-tuning Framework for Optimal Performance and Power Consumption

CHARM-SYCL: New Unified Programming Environment for Multiple Accelerator Types

ExaNBody: a HPC framework for N-Body applications

AFOCL: Portable OpenCL Programming of FPGAs via Automated Built-in Kernel Management

On the Three P’s of Parallel Programming for Heterogeneous Computing: Performance, Productivity, and Portability

Recent source codes

Kokkidio: Kokkos+Eigen - write fast, readable multi-platform code

LLaMA-Inference-Bench

MATAR

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

MaC: Mamba CPU performance predictor

Mixed-precision finite element kernels

SHMT: The Simultaneous and Heterogenous Multithreading project

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

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