high performance computing on graphics processing units: hgpu.org

hgpu.org » Heterogeneous systems

Efficient Matrix Factorization on Heterogeneous CPU-GPU Systems

Yuanhang Yu, Dong Wen, Ying Zhang, Xiaoyang Wang, Wenjie Zhang, Xuemin Lin

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Tags: Algorithms, Computer science, CUDA, Data mining, Factorization, Heterogeneous systems, Machine learning, nVidia, nVidia Quadro P 4000

July 5, 2020 by hgpu

Autotuning for Automatic Parallelization on Heterogeneous Systems

Philip Pfaffe

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Tags: Auto-Tuning, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 970, OpenMP, Performance, Tesla P100, Thesis

June 21, 2020 by hgpu

FPGA Based Satisfiability Checking

Rishi Bharadwaj Subramanian

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Tags: Computer science, Electronic design automation, FPGA, Heterogeneous systems, OpenCL, Thesis

June 21, 2020 by hgpu

Heterogeneous Parallelization and Acceleration of Molecular Dynamics Simulations in GROMACS

Szilárd Páll, Artem Zhmurov, Paul Bauer, Mark Abraham, Magnus Lundborg, Alan Gray, Berk Hess, Erik Lindahl

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Tags: Algorithms, AMD Radeon Instinct Mi50, AMD Vega FE, ATI, Chemistry, CUDA, Heterogeneous systems, Molecular dynamics, MPI, nVidia, nVidia GeForce RTX 2080, nVidia Quadro P 6000, OpenCL, Package, Tesla V100

June 21, 2020 by hgpu

The Rodinia Benchmark Suite in SYCL

Zheming Jin

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Tags: Benchmarking, Computer science, Heterogeneous systems, OpenCL, Package, SYCL

June 14, 2020 by hgpu

Software Testing – Test Suite Compilation and Execution Optimizations

Panagiotis Stratis

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Tags: ATI, ATI Radeon HD 5450, Benchmarking, Computer science, Heterogeneous systems, Optimization, SYCL, Thesis

June 14, 2020 by hgpu

OpenABLext: An automatic code generation framework for agent-based simulations on CPU-GPU-FPGA heterogeneous platforms

Jiajian Xiao, Philipp Andelfinger, Wentong Cai, Paul Richmond, Alois Knoll, David Eckhoff

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Tags: Code generation, Computer science, FPGA, Heterogeneous systems, nVidia, nVidia GeForce GTX 1060, nVidia GeForce GTX 2080 Ti, OpenCL, Package

June 7, 2020 by hgpu

Investigating Single Precision Floating General Matrix Multiply in Heterogeneous

Steven Harris

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Tags: ASIC, Computer science, FPGA, Heterogeneous systems, Matrix multiplication, OpenCL, Performance, performance portability, Thesis

June 7, 2020 by hgpu

Evaluating the performance of HPC-style SYCL applications

Tom Deakin, Simon McIntosh-Smith

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Tags: Benchmarking, Computer science, Heterogeneous systems, OpenCL, OpenMP, Package, Performance, SYCL

May 31, 2020 by hgpu

HetPipe: Enabling Large DNN Training on (Whimpy) Heterogeneous GPU Clusters through Integration of Pipelined Model Parallelism and Data Parallelism

Jay H. Park, Gyeongchan Yun, Chang M. Yi, Nguyen T. Nguyen, Seungmin Lee, Jaesik Choi, Sam H. Noh, Young-ri Choi

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Tags: Computer science, CUDA, Data parallelism, Deep learning, GPU cluster, Heterogeneous systems, Neural networks, nVidia, nVidia GeForce GTX Titan V, nVidia GeForce RTX 2060, nVidia Quadro P 4000, nVidia Titan RTX

May 31, 2020 by hgpu

HaoCL: Harnessing Large-scale Heterogeneous Processors Made Easy

Yao Chen, Xin Long, Jiong He, Yuhang Chen, Hongshi Tan, Zhenxiang Zhang, Marianne Winslett, Deming Chen

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Tags: Computer science, Deep learning, FPGA, Heterogeneous systems, Machine learning, nVidia, OpenCL, Tesla P4

May 24, 2020 by hgpu

SYCL-Bench: A Versatile Single-Source Benchmark Suite for Heterogeneous Computing

Sohan Lal, Aksel Alpay, Philip Salzmann, Biagio Cosenza, Nicolai Stawinoga, Peter Thoman, Thomas Fahringer, Vincent Heuveline

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Tags: Benchmarking, Computer science, FPGA, Heterogeneous systems, Package, Performance, SYCL

May 24, 2020 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

Efficient Matrix Factorization on Heterogeneous CPU-GPU Systems

Autotuning for Automatic Parallelization on Heterogeneous Systems

FPGA Based Satisfiability Checking

Heterogeneous Parallelization and Acceleration of Molecular Dynamics Simulations in GROMACS

The Rodinia Benchmark Suite in SYCL

Software Testing – Test Suite Compilation and Execution Optimizations

OpenABLext: An automatic code generation framework for agent-based simulations on CPU-GPU-FPGA heterogeneous platforms

Investigating Single Precision Floating General Matrix Multiply in Heterogeneous

Evaluating the performance of HPC-style SYCL applications

HetPipe: Enabling Large DNN Training on (Whimpy) Heterogeneous GPU Clusters through Integration of Pipelined Model Parallelism and Data Parallelism

HaoCL: Harnessing Large-scale Heterogeneous Processors Made Easy

SYCL-Bench: A Versatile Single-Source Benchmark Suite for Heterogeneous 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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