high performance computing on graphics processing units: hgpu.org

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Subdivision Surface Evaluation as Sparse Matrix-Vector Multiplication

Michael Driscoll

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Tags: 3D Graphics and Realism, Computer science, CUDA, Linear Algebra, Matrix multiplication, nVidia, nVidia GeForce GTX 480, OpenCL, Package, Sparse matrix

January 5, 2015 by hgpu

Hierarchical DAG Scheduling for Hybrid Distributed Systems

Wei Wu, Aurelien Bouteiller, George Bosilca, Mathieu Faverge, Jack Dongarra

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Tags: Algorithms, Computer science, CUDA, Factorization, Linear Algebra, nVidia, Tesla K40, Tesla M2090

January 5, 2015 by hgpu

Methods and Metrics for Fair Server Assessment under Real-Time Financial Workloads

Giorgis Georgakoudis, Charles J. Gillan, Ahmed Sayed, Ivor Spence, Richard Faloon, Dimitrios S. Nikolopoulos

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Tags: ARM, Computer science, Finance, Intel Xeon Phi, Option pricing

January 5, 2015 by hgpu

GPU: Power vs Performance

Siddhartha Sankar Mondal

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Tags: Benchmarking, Computer science, CUDA, Energy-efficient computing, nVidia, nVidia GeForce GTX 580, Thesis

January 5, 2015 by hgpu

Real-Time Incompressible Fluid Simulation on the GPU

Xiao Nie, Leiting Chen, Tao Xiang

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Tags: 3D Graphics and Realism, CUDA, Fluid dynamics, Fluid simulation, nVidia, nVidia GeForce GTX 480, nVidia GeForce GTX 780, Real-time graphics, Sorting

January 2, 2015 by hgpu

Customization of OpenCL Applications for Efficient Task Mapping under Heterogeneous Platform Constraints

Edoardo Paone, Francesco Robino, Gianluca Palermo, Vittorio Zaccaria, Ingo Sander, Cristina Silvano

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Tags: Computer science, Heterogeneous systems, nVidia, nVidia Quadro NVS 300, OpenCL, Performance

January 2, 2015 by hgpu

Disjunctive Normal Networks

Mehdi Sajjadi, Mojtaba Seyedhosseini, Tolga Tasdizen

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Tags: Computer science, Computer vision, CUDA, Machine learning, Neural and Evolutionary Computing, Neural networks, nVidia

January 2, 2015 by hgpu

Characterization of OpenCL on a Scalable FPGA Architecture

Shanyuan Gao, Jeremy Chritz

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Tags: AES, Computer science, FPGA, OpenCL

December 30, 2014 by hgpu

Extending OmpSs to support CUDA and OpenCL in C, C++ and Fortran Applications

Florentino Sainz, Sergi Mateo, Vicenc Beltran, Jose L. Bosque, Xavier Martorell, Eduard Ayguade

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Tags: Computer science, CUDA, Fortran, nVidia, OpenCL, Tesla M2050

December 30, 2014 by hgpu

A Tool for Automatic Suggestions for Irregular GPU Kernel Optimization

Saeed Taheri

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, Tesla K20, Thesis

December 30, 2014 by hgpu

Spectral classification using convolutional neural networks

Pavel Hala

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Tags: Astrophysics, Computer science, CUDA, Instrumentation and Methods for Astrophysics, Machine learning, Neural and Evolutionary Computing, nVidia, Thesis

December 30, 2014 by hgpu

How to Correctly Deal With Pseudorandom Numbers in Manycore Environments – Application to GPU programming with Shoverand

Jonathan Passerat-Palmbach (ISIMA, UBP, LIMOS), David Hill (LIMOS, UBP, ISIMA)

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Tags: Computer science, CUDA, nVidia, Package, Pseudo-random number generators, Stochastic simulation

December 30, 2014 by hgpu

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

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

True 4-Bit Quantized CNN Training on CPU

True 4-Bit Quantized Convolutional Neural Network Training on CPU: Achieving Full-Precision Parity

cuFuzz: A GPU-oriented coverage-guided fuzzer for userland CUDA application

Hunting CUDA Bugs at Scale with cuFuzz

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high performance computing on graphics processing units: hgpu.org

Applications

Subdivision Surface Evaluation as Sparse Matrix-Vector Multiplication

Hierarchical DAG Scheduling for Hybrid Distributed Systems

Methods and Metrics for Fair Server Assessment under Real-Time Financial Workloads

GPU: Power vs Performance

Real-Time Incompressible Fluid Simulation on the GPU

Customization of OpenCL Applications for Efficient Task Mapping under Heterogeneous Platform Constraints

Disjunctive Normal Networks

Characterization of OpenCL on a Scalable FPGA Architecture

Extending OmpSs to support CUDA and OpenCL in C, C++ and Fortran Applications

A Tool for Automatic Suggestions for Irregular GPU Kernel Optimization

Spectral classification using convolutional neural networks

How to Correctly Deal With Pseudorandom Numbers in Manycore Environments – Application to GPU programming with Shoverand

Recent source codes

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

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

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