high performance computing on graphics processing units: hgpu.org

hgpu.org » Computer science

Accelerating linpack with CUDA on heterogenous clusters

Massimiliano Fatica

Tags: Computer science, CUDA, GPU cluster, Linear Algebra, nVidia

January 11, 2011 by hgpu

FCUDA: Enabling Efficient Compilation of CUDA Kernels onto FPGAs

Ros Papakonstantinou, Karthik Gururaj, John A. Stratton, Deming Chen, Jason Cong, Wen-mei W. Hwu

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Tags: Compilers, Computer science, CUDA, FPGA, High-level Languages

January 11, 2011 by hgpu

High-performance CUDA kernel execution on FPGAs

Alexandros Papakonstantinou, Karthik Gururaj, John A. Stratton, Deming Chen, Jason Cong, Wen Mei

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Tags: Computer science, CUDA, FPGA, Performance, Programming techniques

January 11, 2011 by hgpu

High-speed volume ray casting with CUDA

L. Marsalek, A. Hauber, P. Slusallek

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Tags: Computer science, CUDA, nVidia, Poster, Real-time graphics

January 10, 2011 by hgpu

Comparing Hardware Accelerators in Scientific Applications: A Case Study

Rick Weber, Akila Gothandaraman, Robert J. Hinde, Gregory D. Peterson

Tags: ATI, Brook, Computer science, CUDA, FPGA, nVidia, OpenCL, Performance

January 10, 2011 by hgpu

Neville elimination on multi- and many-core systems: OpenMP, MPI and CUDA

P. Alonso, R. Cortina, F. Martinez-Zaldivar, J. Ranilla

Tags: Computer science, CUBLAS, CUDA, MPI, nVidia, OpenMP

January 10, 2011 by hgpu

Importance sampling algorithms for first passage time probabilities in the infinite server queue

Ad Ridder

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Tags: Algorithms, Computer science, Monte Carlo simulation

January 10, 2011 by hgpu

Dense optical flow by iterative local window registration

G. Le Besnerais, F. Champagnat

Tags: Computer science, CUDA, nVidia, Optical flow

January 10, 2011 by hgpu

Connected component labeling on a 2D grid using CUDA

Oleksandr Kalentev, Abha Rai, Stefan Kemnitz, Ralf Schneider

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Tags: Algorithms, Computer science, CUDA, nVidia

January 10, 2011 by hgpu

Parallel Prefix Sum (Scan) with CUDA

Mark Harris, Shubhabrata Sengupta, John D. Owens

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce 8800 GTX, Visualization

January 9, 2011 by hgpu

Evaluation and tuning of the Level 3 CUBLAS for graphics processors

S. Barrachina, M. Castillo, F. D. Igual, R. Mayo, E. S. Quintana-Orti

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Tags: Computer science, CUBLAS, CUDA, Linear Algebra, nVidia, nVidia GeForce 8800 Ultra

January 9, 2011 by hgpu

Parallel programming for multimedia applications

Hari Kalva, Aleksandar Colic, Adriana Garcia, Borko Furht

Tags: Computer science, CUDA, nVidia, OpenMP, Programming techniques

January 9, 2011 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

Accelerating linpack with CUDA on heterogenous clusters

FCUDA: Enabling Efficient Compilation of CUDA Kernels onto FPGAs

High-performance CUDA kernel execution on FPGAs

High-speed volume ray casting with CUDA

Comparing Hardware Accelerators in Scientific Applications: A Case Study

Neville elimination on multi- and many-core systems: OpenMP, MPI and CUDA

Importance sampling algorithms for first passage time probabilities in the infinite server queue

Dense optical flow by iterative local window registration

Connected component labeling on a 2D grid using CUDA

Parallel Prefix Sum (Scan) with CUDA

Evaluation and tuning of the Level 3 CUBLAS for graphics processors

Parallel programming for multimedia applications

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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