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Adaptive Optimization Techniques for High-Performance Computing

Gulsum Gudukbay Akbulut

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Tags: Computer science, CUDA, Deep learning, HPC, Machine learning, nVidia, Optimization, Performance, Tesla K80, Thesis

January 27, 2025 by hgpu

Dissecting the NVIDIA Hopper Architecture through Microbenchmarking and Multiple Level Analysis

Weile Luo, Ruibo Fan, Zeyu Li, Dayou Du, Hongyuan Liu, Qiang Wang, Xiaowen Chu

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Tags: Benchmarking, Computer science, CUDA, Hardware Architecture, nVidia, nVidia A100, nVidia GeForce RTX 4090, nVidia H800, Performance, PTX

January 27, 2025 by hgpu

Code Generation for Cryptographic Kernels using Multi-word Modular Arithmetic on GPU

Naifeng Zhang, Franz Franchetti

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Tags: Code generation, Computer science, CUDA, Linear Algebra, Modular arithmetic, nVidia, nVidia GeForce RTX 4090, nVidia H100, nVidia V100, Package, Security

January 20, 2025 by hgpu

GSParLib: A multi-level programming interface unifying OpenCL and CUDA for expressing stream and data parallelism

Dinei A. Rockenbach, Gabriell Araujo, Dalvan Griebler, Luiz Gustavo Fernandes

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Tags: Computer science, CUDA, Data parallelism, nVidia, nVidia GeForce RTX 3090, OpenCL, OpenMP, Package

January 20, 2025 by hgpu

A User’s Guide to KSig: GPU-Accelerated Computation of the Signature Kernel

Csaba Tóth, Danilo Jr Dela Cruz, Harald Oberhauser

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Tags: Computer science, CUDA, Machine learning, nVidia, nVidia A100, Package, Python

January 20, 2025 by hgpu

Boosting Performance of Iterative Applications on GPUs: Kernel Batching with CUDA Graphs

Jonah Ekelund, Stefano Markidis, Ivy Peng

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Tags: Computer science, CUDA, FDTD, HIP, nVidia, nVidia A100, nVidia H100, Package, Performance

January 20, 2025 by hgpu

Keras Sig: Efficient Path Signature Computation on GPU in Keras 3

Rémi Genet, Hugo Inzirillo

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Tags: Computer science, CUDA, Deep learning, Machine learning, nVidia, nVidia GeForce RTX 4090, Package, Python, TensorFlow

January 20, 2025 by hgpu

SCALE-Ahead-Of-Time Compilation of CUDA for AMD GPUs

Manos Pavlidakis, Chris Kitching, Nicholas Tomlinson, Michael Søndergaard

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Tags: ATI, Compilers, Computer science, CUDA, HIP, nVidia, Package, Portability, PTX

January 13, 2025 by hgpu

CGP-Tuning: Structure-Aware Soft Prompt Tuning for Code Vulnerability Detection

Ruijun Feng, Hammond Pearce, Pietro Liguori, Yulei Sui

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Tags: Computer science, CUDA, LLM, nVidia, nVidia H100, Python, PyTorch, Security

January 13, 2025 by hgpu

Data Parallel Visualization and Rendering on the RAMSES Supercomputer with ANARI

Stefan Zellmann

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Tags: 3D Graphics and Realism, HPC, MPI, nVidia, nVidia H100, Package, Rendering, Visualization

January 13, 2025 by hgpu

Validation of GPU Computation in Decentralized, Trustless Networks

Eric Boniardi, Stanley Bishop, Alison Haire

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Tags: Computer science, Distributed computing

January 13, 2025 by hgpu

LeetDecoding: A PyTorch Library for Exponentially Decaying Causal Linear Attention with CUDA Implementations

Jiaping Wang, Simiao Zhang, Qiao-Chu He, Yifan Chen

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Tags: Benchmarking, Computer science, CUDA, LLM, Machine learning, nVidia, nVidia A100, nVidia RTX A6000, Package, Python, PyTorch

January 13, 2025 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

Applications

Adaptive Optimization Techniques for High-Performance Computing

Dissecting the NVIDIA Hopper Architecture through Microbenchmarking and Multiple Level Analysis

Code Generation for Cryptographic Kernels using Multi-word Modular Arithmetic on GPU

GSParLib: A multi-level programming interface unifying OpenCL and CUDA for expressing stream and data parallelism

A User’s Guide to KSig: GPU-Accelerated Computation of the Signature Kernel

Boosting Performance of Iterative Applications on GPUs: Kernel Batching with CUDA Graphs

Keras Sig: Efficient Path Signature Computation on GPU in Keras 3

SCALE-Ahead-Of-Time Compilation of CUDA for AMD GPUs

CGP-Tuning: Structure-Aware Soft Prompt Tuning for Code Vulnerability Detection

Data Parallel Visualization and Rendering on the RAMSES Supercomputer with ANARI

Validation of GPU Computation in Decentralized, Trustless Networks

LeetDecoding: A PyTorch Library for Exponentially Decaying Causal Linear Attention with CUDA Implementations

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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