high performance computing on graphics processing units: hgpu.org

Programming

hgpu.org » Programming

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

Mashing load balancing algorithm to boost hybrid kernels in molecular dynamics simulations

Raúl Nozal, Jose Luis Bosque

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Tags: AMD Radeon RX 5700 XT, ATI, Computer science, Heterogeneous systems, HPC, Molecular dynamics, OpenCL, OpenMP, Package, performance portability

August 28, 2023 by hgpu

APACE: AlphaFold2 and advanced computing as a service for accelerated discovery in biophysics

Hyun Park, Parth Patel, Roland Haas, E. A. Huerta

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Tags: AI, Algorithms, Biology, Biomolecules, Biophysics, nVidia, nVidia A100, nVidia A40, Package

August 20, 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

Increased reliability on Intel GPUs via software diverse redundancy

Nikolaos Andriotis

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Tags: Computer science, Deep learning, Machine learning, MPI, nVidia, OpenCL, Thesis

August 20, 2023 by hgpu

Generating Parallel OpenCL and OpenMP Programs from Dataflow Graphs

Evelyn Borth

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Tags: Benchmarking, Code generation, Computer science, OpenCL, OpenMP, Thesis

August 20, 2023 by hgpu

Static and Dynamic Analyses for Efficient GPU Execution

Philip Munksgaard

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Tags: AMD Radeon Instinct MI100, ATI, Benchmarking, Computer science, nVidia, nVidia A100, OpenCL, Performance, Thesis

August 13, 2023 by hgpu

Monadic Deep Learning

Bo Yang, Zhihao Zhang Kirisame Marisa, Kai Shi

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Tags: Computer science, CUDA, Deep learning, Java, Neural networks, OpenCL, Package, Scala

July 30, 2023 by hgpu

Bandicoot: C++ Library for GPU Linear Algebra and Scientific Computing

Ryan R. Curtin, Marcus Edel, Conrad Sanderson

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Tags: Computer science, CUDA, Linear Algebra, nVidia, nVidia GeForce RTX 2080 Ti, OpenCL, Package, Programming techniques

July 30, 2023 by hgpu

Efficiency without Tears: Securing Multilingual Programs with TRINITY

Hao Zhu, Baojian Hua, Xinrong Lin, Yufei Wu

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Tags: Computer science, Databases, Heterogeneous systems, HPC, Machine learning, OpenCL, Security

July 30, 2023 by hgpu

Fast Knowledge Graph Completion using Graphics Processing Units

Chun-Hee Lee, Dong-oh Kang, Hwa Jeon Song

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Tags: AI, Algorithms, Computer science, CUDA, Databases, Graph theory, nVidia, nVidia A100

July 30, 2023 by hgpu

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

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

BoltzGen:Toward Universal Binder Design

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

tritonBLAS: A Lightweight Triton-based General Matrix Multiplication (GEMM) Library

tritonBLAS: Triton-based Analytical Approach for GEMM Kernel Parameter Selection

See all packages

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high performance computing on graphics processing units: hgpu.org

Programming

Sieve: Stratified GPU-Compute Workload Sampling

Mashing load balancing algorithm to boost hybrid kernels in molecular dynamics simulations

APACE: AlphaFold2 and advanced computing as a service for accelerated discovery in biophysics

Porting Batched Iterative Solvers onto Intel GPUs with SYCL

Quantifying OpenMP: Statistical Insights into Usage and Adoption

Increased reliability on Intel GPUs via software diverse redundancy

Generating Parallel OpenCL and OpenMP Programs from Dataflow Graphs

Static and Dynamic Analyses for Efficient GPU Execution

Monadic Deep Learning

Bandicoot: C++ Library for GPU Linear Algebra and Scientific Computing

Efficiency without Tears: Securing Multilingual Programs with TRINITY

Fast Knowledge Graph Completion using Graphics Processing Units

Recent source codes

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

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

BoltzGen:Toward Universal Binder Design

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

tritonBLAS: A Lightweight Triton-based General Matrix Multiplication (GEMM) Library

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