high performance computing on graphics processing units: hgpu.org

Programming

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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

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

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

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

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

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

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

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

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

GDlog: A GPU-Accelerated Deductive Engine

Yihao Sun, Ahmedur Rahman Shovon, Thomas Gilray, Kristopher Micinski, Sidharth Kumar

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Tags: Computer science, CUDA, Databases, nVidia, nVidia A100

November 12, 2023 by hgpu

KernelBenchX: A Comprehensive Benchmark for Evaluating LLM-Generated GPU Kernels

CuTile Benchmark Suite: Performance and Productivity Tradeoffs for GPU Kernel Programming on Blackwell Architecture

Evaluating CUDA Tile for AI Workloads on Hopper and Blackwell GPUs

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

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

Programming

RDMA-Based Algorithms for Sparse Matrix Multiplication on GPUs

Evaluation of FPGA-based high performance computing platforms

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

Accelerating bioinformatics applications on CUDA-enabled multi-GPU systems

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

GPU Auto-tuning Framework for Optimal Performance and Power Consumption

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

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

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

ExaNBody: a HPC framework for N-Body applications

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

GDlog: A GPU-Accelerated Deductive Engine

Recent source codes

KernelBenchX: A Comprehensive Benchmark for Evaluating LLM-Generated GPU Kernels

CUDA Kernel Fusion Benchmarks

IntelliKit: Agent-first tooling for AMD hardware

DITRON: Distributed Compiler based on Triton for Parallel Systems

CuTile Benchmark Suite: Performance and Productivity Tradeoffs for GPU Kernel Programming on Blackwell Architecture

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

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context

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