high performance computing on graphics processing units: hgpu.org

Aristotle Martin, Geng Liu, William Ladd, Seyong Lee, John Gounley, Jeffrey Vetter, Saumil Patel, Silvio Rizzi, Victor Mateevitsi, Joseph Insley, Amanda Randles

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Tags: AMD Radeon Instinct MI250X, ATI, cfd, CUDA, Fluid dynamics, Heterogeneous systems, HIP, nVidia, nVidia A100, nVidia V100, performance portability, SYCL

December 31, 2023 by hgpu

Enabling Quantum Computer Simulations on AMD GPUs: a HIP Backend for Google’s qsim

Stefano Markidis

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Tags: AMD Radeon Instinct MI250X, Computer science, CUDA, HIP, nVidia, nVidia A100, Quantum computing

December 31, 2023 by hgpu

Gaiwan: a Size-Polymorphic Typesystem for GPU Programs

Robbert Gurdeep Singha, Christophe Scholliers

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Source codes

Tags: Benchmarking, Computer science, nVidia, nVidia A100, OpenCL, Package, Programming Languages

December 31, 2023 by hgpu

KeSCo: Compiler-based Kernel Scheduling for Multi-task GPU Applications

Zejia Lin, Zewei Mo, Xuanteng Huang, Xianwei Zhang, Yutong Lu

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Tags: Benchmarking, Compilers, Computer science, CUDA, nVidia, nVidia A100, Task scheduling

December 24, 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

Preliminary report: Initial evaluation of StdPar implementations on AMD GPUs for HPC

HiRace: Accurate and Fast Source-Level Race Checking of GPU Programs

Code Generation for a Variety of Accelerators for a Graph DSL

Deep Learning for Obfuscated Code Analysis

UniFL: Accelerating Federated Learning Using Heterogeneous Hardware Under a Unified Framework

An Autonomous Data Language

Adding fault tolerance to OpenCL: Through redundant heterogeneous computing

Optimization of Ported CFD Kernels on Intel Data Center GPU Max 1550 using oneAPI ESIMD

Performance Evaluation of Heterogeneous GPU Programming Frameworks for Hemodynamic Simulations

Enabling Quantum Computer Simulations on AMD GPUs: a HIP Backend for Google’s qsim

Gaiwan: a Size-Polymorphic Typesystem for GPU Programs

KeSCo: Compiler-based Kernel Scheduling for Multi-task GPU Applications

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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