high performance computing on graphics processing units: hgpu.org

Applications

hgpu.org » Applications » Computer science

Rapid Performance of a Generalized Distance Calculation

Scott Fisackerly, Eric Chu, David L. Foster

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Tags: Algorithms, Clustering, Computer science, CUDA, nVidia, nVidia GeForce GTX 480, Tesla C1060

October 31, 2011 by hgpu

Low Latency Complex Event Processing on Parallel Hardware

Gianpaolo Cugola, Alessandro Margara

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Tags: Algorithms, Computer science, CUDA, Filtering, nVidia, nVidia GeForce GTX 460

October 31, 2011 by hgpu

Fast Speaker Diarization Using a High-Level Scripting Language

Ekaterina Gonina, Gerald Friedland, Henry Cook, Kurt Keutzer

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Tags: Clustering, Computer science, CUDA, nVidia, nVidia GeForce GTX 480, Python, Speech recognition

October 31, 2011 by hgpu

Workload Balancing on Heterogeneous Systems: A Case Study of Sparse Grid Interpolation

Alin Murarasu, Josef Weidendorfer, Arndt Bode

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Tags: Compression, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 480, Task scheduling, Tesla C2050

October 31, 2011 by hgpu

Environment Segmentation in Service Robotics

Matthew S. Roscoe, Paul G Ploger

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Tags: Algorithms, Computer science, Computer vision, CUDA, nVidia, Tesla S2070

October 31, 2011 by hgpu

High-performance software rasterization on GPUs

Samuli Laine, Tero Karras

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Source codes

Tags: 3D Graphics and Realism, Computer science, CUDA, nVidia, nVidia GeForce GTX 480, Package, Rasterization

October 31, 2011 by hgpu

Parallel implematation of flow and matching algorithms

Agnieszka Lupinska

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Tags: Algorithms, Computer science, CUDA, Graph theory, nVidia, nVidia GeForce GTX 560 Ti

October 31, 2011 by hgpu

Automatic CUDA Code Synthesis Framework for Multicore CPU and GPU architectures

Hanwoong Jung, Youngmin Yi, Soonhoi Ha

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Tags: Code generation, Computer science, CUDA, Data parallelism, Heterogeneous systems, nVidia, Tesla M2050

October 30, 2011 by hgpu

A scalable hybrid algorithm based on domain decomposition and algebraic multigrid for solving partial differential equations on a cluster of CPU/GPUs

Li Luo, Chao Yang, Yubo Zhao, Xiao-Chuan Cai

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Tags: Algorithms, Computer science, CUDA, Differential equations, Factorization, FFT, Finite difference, GPU cluster, nVidia, Partial differential equations, PDEs, Tesla C1060

October 30, 2011 by hgpu

Efficient Implementation of the eta_T Pairing on GPU

Yosuke Katoh, Yun-Ju Huang, Chen-Mou Cheng, Tsuyoshi Takagi

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Tags: Algorithms, Computer science, nVidia, nVidia GeForce GTX 285, nVidia GeForce GTX 480, Security, Tesla C1060, Tesla C2050

October 30, 2011 by hgpu

Optimizing and Auto-tuning Belief Propagation on the GPU

Scott Grauer-Gray, John Cavazos

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Tags: Compilers, Computer science, CUDA, nVidia, nVidia GeForce GTX 285, nVidia GeForce GTX 470, Optimization, Tesla C870

October 30, 2011 by hgpu

Intermediate Language Extensions for Parallelism

Jisheng Zhao, Vivek Sarkar

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Tags: Computer science, Java, Optimization, Programming techniques

October 30, 2011 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

See all packages

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high performance computing on graphics processing units: hgpu.org

Applications

Rapid Performance of a Generalized Distance Calculation

Low Latency Complex Event Processing on Parallel Hardware

Fast Speaker Diarization Using a High-Level Scripting Language

Workload Balancing on Heterogeneous Systems: A Case Study of Sparse Grid Interpolation

Environment Segmentation in Service Robotics

High-performance software rasterization on GPUs

Parallel implematation of flow and matching algorithms

Automatic CUDA Code Synthesis Framework for Multicore CPU and GPU architectures

A scalable hybrid algorithm based on domain decomposition and algebraic multigrid for solving partial differential equations on a cluster of CPU/GPUs

Efficient Implementation of the eta_T Pairing on GPU

Optimizing and Auto-tuning Belief Propagation on the GPU

Intermediate Language Extensions for Parallelism

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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