high performance computing on graphics processing units: hgpu.org

Xiaojie Wu, Qiming Sun, Zhichen Pu, Tianze Zheng, Wenzhi Ma, Wen Yan, Xia Yu, Zhengxiao Wu, Mian Huo, Xiang Li, Weiluo Ren, Sheng Gong, Yumin Zhang, Weihao Gao

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Tags: Chemical Physics, Chemistry, Computational Physics, CUDA, nVidia, nVidia A100, Package, Python, Quantum Physics

April 21, 2024 by hgpu

QArray: a GPU-accelerated constant capacitance model simulator for large quantum dot arrays

Barnaby van Straaten, Joseph Hickie, Lucas Schorling, Jonas Schuff, Federico Fedele, Natalia Ares

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Tags: Condensed matter, Machine learning, Mesoscale and Nanoscale Physics, nVidia, nVidia GeForce GTX 1080 Ti, Package, Physics, Python, Rust

April 14, 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

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

Gaining Cross-Platform Parallelism for HAL’s Molecular Dynamics Package using SYCL

Fast and Practical Strassen’s Matrix Multiplication using FPGAs

Machine learning enhanced code optimization for high-level synthesis (ML-ECOHS)

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

CuPBoP: Making CUDA a Portable Language

Automated Deep Learning Optimization via DSL-Based Source Code Transformation

Experiences with implementing Kokkos’ SYCL backend

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

Python-Based Quantum Chemistry Calculations with GPU Acceleration

QArray: a GPU-accelerated constant capacitance model simulator for large quantum dot arrays

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

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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