high performance computing on graphics processing units: hgpu.org

Tommaso Biagini, Giovanni Chillemi, Gianluigi Mazzoccoli, Alessandro Grottesi, Caterina Fusilli, Daniele Capocefalo, Stefano Castellana, Angelo Luigi Vescovi, Tommaso Mazza

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Tags: Benchmarking, Bioinformatics, Biology, Chemistry, CUDA, Genomics, Molecular dynamics, nVidia, OpenCL, Physics, Tesla C2070

December 19, 2017 by hgpu

Accelerated Sparse Matrix Operations in Nonlinear Least Squares Solvers

Lukas Polok

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Tags: Algorithms, Computer science, Differential equations, Factorization, FEM, Finite element method, Linear Algebra, nVidia, nVidia GeForce GTX 680, OpenCL, Partial differential equations, PDEs, Sparse matrix, Tesla K40, Thesis

December 19, 2017 by hgpu

POMPEI: Programming with OpenMP4 for Exascale Investigations

Jack Dongarra, Azzam Haidar, Oscar Hernandez, Stanimire Tomov, Manjunath Gorentla Venkata

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Tags: Computer science, Intel Xeon Phi, Linear Algebra, nVidia, OpenACC, OpenMP, Programming techniques, Tesla M2090

December 15, 2017 by hgpu

Intra-node Memory Safe GPU Co-Scheduling

Carlos Reano, Federico Silla, Dimitrios S. Nikolopoulos, Blesson Varghese

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Tags: Computer science, CUDA, nVidia, Package, Task scheduling, Tesla K20

December 15, 2017 by hgpu

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

Agentic Code Optimization via Compiler-LLM Cooperation

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

LLM.Q: Quantized LLM training in pure CUDA/C++

LLMQ: Efficient Lower-Precision LLM Training for Consumer GPUs

SOL-ExecBench: Speed-of-Light Benchmarking for Real-World GPU Kernels Against Hardware Limits

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high performance computing on graphics processing units: hgpu.org

Analysing the Performance of GPU Hash Tables for State Space Exploration

Design and Implementation of the Futhark Programming Language

A Generic Inverted Index Framework for Similarity Search on the GPU

Protecting Real-Time GPU Applications on Integrated CPU-GPU SoC Platforms

Extending OmpSs for OpenCL kernel co-execution in heterogeneous systems

An MPI-Based Python Framework for Distributed Training with Keras

An In-depth Performance Characterization of CPU- and GPU-based DNN Training on Modern Architectures

GAMER-2: a GPU-accelerated adaptive mesh refinement code — accuracy, performance, and scalability

Molecular dynamics recipes for genome research

Accelerated Sparse Matrix Operations in Nonlinear Least Squares Solvers

POMPEI: Programming with OpenMP4 for Exascale Investigations

Intra-node Memory Safe GPU Co-Scheduling

Recent source codes

MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU

Device Virtual Machine (DVM)

Agentic Code Optimization via Compiler-LLM Cooperation

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

LLM.Q: Quantized LLM training in pure CUDA/C++

SOL-ExecBench: Speed-of-Light Benchmarking for Real-World GPU Kernels Against Hardware Limits

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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