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Evaluation of computational and energy performance in matrix multiplication algorithms on CPU and GPU using MKL, cuBLAS and SYCL

L.A. Torres, Carlos J. Barrios H, Yves Denneulin

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Tags: Computer science, CUBLAS, CUDA, Linear Algebra, Matrix multiplication, Neural networks, nVidia, nVidia A100, Package, Performance, SYCL

June 2, 2024 by hgpu

Automated Deep Learning Optimization via DSL-Based Source Code Transformation

Ruixin Wang, Minghai Lu, Cody Hao Yu, Yi-Hsiang Lai, Tianyi Zhang

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Tags: Computer science, CUDA, Deep learning, DSL, nVidia, Package, Performance, PyTorch, Tesla T4

May 12, 2024 by hgpu

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

Experiences with implementing Kokkos’ SYCL backend

Daniel Arndt, Damien Lebrun-Grandie, Christian Trott

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Tags: Benchmarking, Computer science, HPC, Intel, Intel Data Center GPU Max 1550, Package, performance portability

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

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

Erik D. Huckvale, Hunter N.B. Moseley

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Tags: Computer science, CUDA, nVidia, Package, Performance, Profiling, Python

April 7, 2024 by hgpu

LOOPer: A Learned Automatic Code Optimizer For Polyhedral Compilers

Massinissa Merouani, Khaled Afif Boudaoud, Iheb Nassim Aouadj, Nassim Tchoulak, Islam Kara Bernou, Hamza Benyamina, Fatima Benbouzid-Si Tayeb, Karima Benatchba, Hugh Leather, Riyadh Baghdadi

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Tags: Compilers, Computer science, Deep learning, Machine learning, OpenCL, Package, Programming Languages

March 24, 2024 by hgpu

Retargeting and Respecializing GPU Workloads for Performance Portability

Ivan R. Ivanov, Oleksandr Zinenko, Jens Domke, Toshio Endo, William S. Moses

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Tags: AMD Radeon Instinct MI210, AMD Radeon RX 6800, ATI, Computer science, CUDA, HIP, HPC, nVidia, nVidia A100, nVidia RTX A4000, Package, performance portability

March 24, 2024 by hgpu

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

John Tramm, Paul Romano, Patrick Shriwise, Amanda Lund, Johannes Doerfert, Patrick Steinbrecher, Andrew Siegel, Gavin Ridley

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Tags: AMD Radeon Instinct MI250X, ATI, Computer science, CUDA, Intel, Intel Data Center GPU Max 1550, Intel Ponte Vecchio Max 1100, nVidia, nVidia A100, OpenMP, Package, performance portability

March 24, 2024 by hgpu

Parallel Gaussian process with kernel approximation in CUDA

Davide Carminati

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Tags: Benchmarking, Computer science, CUDA, Linear Algebra, nVidia, nVidia GeForce GTX 1050, nVidia GeForce RTX 2080, Package

March 24, 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

HPCTransCompile: An AI Compiler Generated Dataset for High-Performance CUDA Transpilation and LLM Preliminary Exploration

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Can Large Language Models Predict Parallel Code Performance?

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high performance computing on graphics processing units: hgpu.org

Applications

Evaluation of computational and energy performance in matrix multiplication algorithms on CPU and GPU using MKL, cuBLAS and SYCL

Automated Deep Learning Optimization via DSL-Based Source Code Transformation

CuPBoP: Making CUDA a Portable Language

Experiences with implementing Kokkos’ SYCL backend

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

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

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

LOOPer: A Learned Automatic Code Optimizer For Polyhedral Compilers

Retargeting and Respecializing GPU Workloads for Performance Portability

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

Parallel Gaussian process with kernel approximation in CUDA

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

Recent source codes

HPCTransCompile: An AI Compiler Generated Dataset for High-Performance CUDA Transpilation and LLM Preliminary Exploration

chemtrain: Training Molecular Dynamics Potentials in JAX

microSYCL: SYCL micro-benchmarks repository

XaaS containers

SYCL Container

CASS: Cuda-Amd aSSembly

Cluser of smartphones for edge computing application using TensorFlow

CFAL-bench

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Can Large Language Models Predict Parallel Code Performance?

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