high performance computing on graphics processing units: hgpu.org

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hgpu.org » Applications » Computer science

Verifying CUDA Programs using SMT-Based Context-Bounded Model Checking

Phillipe A. Pereira, Higo F. Albuquerque, Hendrio M. Marques, Isabela S. Silva, Celso B. Carvalho, Lucas C. Cordeiro

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Tags: Computer science, CUDA, nVidia, Package

January 7, 2016 by hgpu

DeepLearningKit – an Open Source Deep Learning Framework for Apple’s iOS, OS X and tvOS developed in Metal and Swift

Amund Tvei, Torbjorn Morland, Thomas Brox Rost

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Tags: Computer science, Deep learning, Machine learning, Neural networks, OpenCL, Package, Vulkan

January 7, 2016 by hgpu

Faster GPU Based Genetic Programming Using A Two Dimensional Stack

Darren M. Chitty

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Tags: Computer science, CUDA, Genetic programming, nVidia, nVidia GeForce GTX 670

January 7, 2016 by hgpu

Batched Linear Algebra Problems on GPU Accelerators

Tingxing Dong

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Tags: Computer science, CUDA, Factorization, Heterogeneous systems, Linear Algebra, nVidia, Tesla K40, Thesis

January 4, 2016 by hgpu

Programming Models and Scheduling Techniques for Heterogeneous Architectures

Judit Planas Carbonell

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Tags: Computer science, Heterogeneous systems, Intel Xeon Phi, nVidia, OmpSs, Task scheduling, Tesla K20, Tesla K40, Tesla M2090, Thesis

January 4, 2016 by hgpu

Automatic Performance Tuning of Stencil Computations on Graphics Processing Units

Joseph D. Garvey

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Tags: Computer science, Machine learning, nVidia, nVidia GeForce GTX Titan, OpenCL, Performance, Stencil computation, Thesis

January 4, 2016 by hgpu

Accelerating Binary Genetic Algorithm Driven Missile Design Optimization Routine with a CUDA Coded Six Degrees-Of-Freedom Simulator

Daniel Benton

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 570

January 4, 2016 by hgpu

A Comparison of the performance of HPC Accelerators

Xiao Li

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Tags: AMD FirePro W9100, ATI, Computer science, nVidia, OpenCL, Performance, Tesla K40, Thesis

December 31, 2015 by hgpu

Study of basic vector operations on Intel Xeon Phi and NVIDIA Tesla using OpenCL

Edoardo Coronado, Guillermo Indalecio, Antonio Garcia-Loureiro

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Tags: Computer science, Intel Xeon Phi, Linear Algebra, nVidia, OpenCL, Performance, Tesla S2050

December 31, 2015 by hgpu

A Deep Generative Deconvolutional Image Model

Yunchen Pu, Xin Yuan, Andrew Stevens, Chunyuan Li, Lawrence Carin

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Tags: Bayesian, Caffe, Computer science, Computer vision, CUDA, Deep learning, Image generation, Machine learning, Neural networks, nVidia, nVidia GeForce GTX Titan X

December 31, 2015 by hgpu

Parallel 3D Fast Wavelet Transform comparison on CPUs and GPUs

Gregorio Bernabe

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Tags: Computer science, CUDA, FFT, nVidia, OpenCL, OpenMP, Pthreads, Tesla C2050

December 31, 2015 by hgpu

Poseidon: A System Architecture for Efficient GPU-based Deep Learning on Multiple Machines

Hao Zhang, Zhiting Hu, Jinliang Wei, Pengtao Xie, Gunhee Kim, Qirong Ho, Eric Xing

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Tags: Caffe, Computer science, Computer vision, Deep learning, GPU cluster, Machine learning, Neural networks, nVidia, Package, Tesla K20

December 23, 2015 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

Verifying CUDA Programs using SMT-Based Context-Bounded Model Checking

DeepLearningKit – an Open Source Deep Learning Framework for Apple’s iOS, OS X and tvOS developed in Metal and Swift

Faster GPU Based Genetic Programming Using A Two Dimensional Stack

Batched Linear Algebra Problems on GPU Accelerators

Programming Models and Scheduling Techniques for Heterogeneous Architectures

Automatic Performance Tuning of Stencil Computations on Graphics Processing Units

Accelerating Binary Genetic Algorithm Driven Missile Design Optimization Routine with a CUDA Coded Six Degrees-Of-Freedom Simulator

A Comparison of the performance of HPC Accelerators

Study of basic vector operations on Intel Xeon Phi and NVIDIA Tesla using OpenCL

A Deep Generative Deconvolutional Image Model

Parallel 3D Fast Wavelet Transform comparison on CPUs and GPUs

Poseidon: A System Architecture for Efficient GPU-based Deep Learning on Multiple Machines

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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