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hgpu.org » Applications » Computer science

Automated Tool to Generate Parallel CUDA code from a Serial C Code

Akhil Jindal, Nikhil Jindal, Divyashikha Sethia

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Tags: Code generation, Computer science, CUDA, nVidia, nVidia GeForce 8400 GS

August 2, 2012 by hgpu

C to Cellular Automata and Execution on CPU, GPU and FPGA

Jens Drieseberg, Christian Siemers

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Tags: Cellular automata, Computer science, CUDA, FPGA, nVidia, nVidia GeForce GTX 580, OpenMP

August 2, 2012 by hgpu

Interference-driven resource management for GPU-based heterogeneous clusters

Rajat Phull, Cheng-Hong Li, Kunal Rao, Srihari Cadambi, Srimat Chakradhar

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Tags: Computer science, CUDA, GPU cluster, Heterogeneous systems, MPI, nVidia, PC cluster, Performance, Tesla C2050

August 1, 2012 by hgpu

GPU merge path: a GPU merging algorithm

Oded Green, Robert McColl, David A. Bader

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Tags: Algorithms, Computer science, CUDA, Databases, nVidia, Tesla C1060, Tesla C2070

August 1, 2012 by hgpu

New Sparse Matrix Storage Format to Improve The Performance of Total SPMV Time

Neelima Reddy, Raghavendra Prakash, Ram Mohana Reddy

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Tags: Computer science, CUDA, Data parallelism, nVidia, nVidia GeForce GTX 470, Sparse matrix

August 1, 2012 by hgpu

High-Level Manipulation of OpenCL-Based Subvectors and Submatrices

Karl Rupp

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Tags: Computer science, Factorization, nVidia, nVidia GeForce GTX 470, OpenCL

August 1, 2012 by hgpu

GPU-Accelerated Non-negative Matrix Factorization for Text Mining

Volodymyr Kysenko, Karl Rupp, Oleksandr Marchenko, Siegfried Selberherr, Anatoly Anisimov

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Tags: Algorithms, ATI, ATI Radeon HD 6970, Computer science, Factorization, Nonnegative matrix factorization, nVidia, nVidia GeForce GTX 470, OpenCL, Text mining

August 1, 2012 by hgpu

accULL: An User-directed Approach to Heterogeneous Programming

Ruyman Reyes, Ivan Lopez, Juan J. Fumero, Francisco de Sande

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Source codes

Tags: Computer science, CUDA, Heterogeneous systems, nVidia, OpenACC, OpenCL, Package, Python, Tesla C1060, Tesla C2050

July 31, 2012 by hgpu

Parallel programming on GPU using Intel Array Building Blocks

Chatura A. Ettigi

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Tags: Computer science, CUDA, Matrix multiplication, nVidia, nVidia GeForce GTX 260 M, Programming techniques, Thesis

July 31, 2012 by hgpu

Application of the Mean Field Methods to MRF Optimization in Computer Vision

Masaki Saito, Takayuki Okatani, Koichiro Deguchi

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Tags: Computer science, Computer vision, CUDA, nVidia, nVidia GeForce GTX 480, OpenMP

July 31, 2012 by hgpu

High-Performance Online Spatial and Temporal Aggregations on Multi-core CPUs and Many-Core GPUs

Jianting Zhang, Simin You, Le Gruenwald

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Tags: Computer science, CUDA, Databases, nVidia, nVidia Quadro FX 6000

July 29, 2012 by hgpu

Sigma*: Symbolic Learning of Stream Filters

Matko Botincan, Domagoj Babic

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Tags: Code generation, Computer science, CUDA, nVidia, nVidia GeForce GTX 460

July 29, 2012 by hgpu

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

KernelSkill: A Multi-Agent Framework for GPU Kernel Optimization

MSKernelBench & CUDAMaster

Making LLMs Optimize Multi-Scenario CUDA Kernels Like Experts

EvoScientist: Harness Vibe Research with Self-evolving AI Scientists

EvoScientist: Towards Multi-Agent Evolving AI Scientists for End-to-End Scientific Discovery

RepoLaunch: Automating Build and Test Pipeline of Code Repositories on ANY Language and ANY Platform

RepoLaunch: Automating Build & Test Pipeline of Code Repositories on ANY Language and ANY Platform

RepoLaunch: Automating Build and Test Pipeline of Code Repositories on ANY Language and ANY Platform

RepoLaunch: Automating Build & Test Pipeline of Code Repositories on ANY Language and ANY Platform

CONCUR: a benchmark designed to evaluate multithreaded Java code generated by LLMs

CONCUR: Benchmarking LLMs for Concurrent Code Generation

HIPRT: Ray Tracing using HIP

Ray Tracing using HIP

MXFP4 Training Support Codebase

Practical FP4 Training for Large-Scale MoE Models on Hopper GPUs

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high performance computing on graphics processing units: hgpu.org

Applications

Automated Tool to Generate Parallel CUDA code from a Serial C Code

C to Cellular Automata and Execution on CPU, GPU and FPGA

Interference-driven resource management for GPU-based heterogeneous clusters

GPU merge path: a GPU merging algorithm

New Sparse Matrix Storage Format to Improve The Performance of Total SPMV Time

High-Level Manipulation of OpenCL-Based Subvectors and Submatrices

GPU-Accelerated Non-negative Matrix Factorization for Text Mining

accULL: An User-directed Approach to Heterogeneous Programming

Parallel programming on GPU using Intel Array Building Blocks

Application of the Mean Field Methods to MRF Optimization in Computer Vision

High-Performance Online Spatial and Temporal Aggregations on Multi-core CPUs and Many-Core GPUs

Sigma*: Symbolic Learning of Stream Filters

Recent source codes

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

MSKernelBench & CUDAMaster

EvoScientist: Harness Vibe Research with Self-evolving AI Scientists

RepoLaunch: Automating Build and Test Pipeline of Code Repositories on ANY Language and ANY Platform

RepoLaunch: Automating Build and Test Pipeline of Code Repositories on ANY Language and ANY Platform

CONCUR: a benchmark designed to evaluate multithreaded Java code generated by LLMs

HIPRT: Ray Tracing using HIP

MXFP4 Training Support Codebase

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