Applications

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gearshifft – The FFT Benchmark Suite for Heterogeneous Platforms

Peter Steinbach, Matthias Werner

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Tags: Algorithms, Benchmarking, Computer science, FFT, Heterogeneous systems, nVidia, nVidia GeForce GTX 1080, OpenCL, Package, Tesla K20, Tesla K80, Tesla P100

February 10, 2017 by hgpu

GraviDy: a GPU modular, parallel N-body integrator

Cristian Maureira-Fredes, Pau Amaro-Seoane

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Tags: Astrophysics, CUDA, Globular clusters, Gravitation, Instrumentation and Methods for Astrophysics, MPI, nVidia, OpenMP, Package, Physics, Tesla C2050, Tesla C2070, Tesla M2050

February 5, 2017 by hgpu

Parallel Implementations of the Cholesky Decomposition on CPUs and GPUs

Joao Paulo Tarasconi Ruschel

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Tags: Algorithms, Benchmarking, Computer science, CUDA, Linear Algebra, Matrix decomposition, nVidia, OpenCL, OpenMP, Package, Performance, Tesla K80, Thesis

January 26, 2017 by hgpu

A GPU-Based Solution to Fast Calculation of Betweenness Centrality on Large Weighted Networks

Rui Fan, Ke Xu, Jichang Zhao

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 980, Package

January 26, 2017 by hgpu

DeepBach: a Steerable Model for Bach chorales generation

Gaetan Hadjeres, Francois Pachet

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Tags: Artificial intelligence, Computer science, CUDA, Deep learning, Keras, nVidia, nVidia GeForce GTX 980 Ti, Package

January 23, 2017 by hgpu

OpenNMT: Open-Source Toolkit for Neural Machine Translation

Guillaume Klein, Yoon Kim, Yuntian Deng, Jean Senellart, Alexander M. Rush

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Tags: Computer science, CUDA, Deep learning, Machine learning, NLP, nVidia, nVidia GeForce GTX 1080, Package, Python, Torch

January 19, 2017 by hgpu

Xeon Phi: A comparison between the newly introduced MIC architecture and a standard CPU through three types of problems

Joakim Kristiansen

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Tags: Algorithms, Computer science, Intel Xeon Phi, Package, Sorting, Thesis

January 19, 2017 by hgpu

Parallelizing Exact and Approximate String Matching via Inclusive Scan on a GPU

Yasuaki Mitani, Fumihiko Ino, Kenichi Hagihara

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX Titan X, Package, String matching

January 19, 2017 by hgpu

An N log N Parallel Fast Direct Solver for Kernel Matrices

Chenhan D. Yu, William B. March, George Biros

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Tags: Algorithms, Computational Complexity, Computer science, Factorization, Intel Xeon Phi, Machine learning, Package, Performance

January 16, 2017 by hgpu

Using efficient parallelization in Graphic Processing Units to parameterize stochastic fire propagation models

Monica Denham, Karina Laneri, Juan Manuel Morales

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Tags: Biology, CUDA, Earth and Space Sciences, nVidia, nVidia GeForce GTX 780, nVidia GeForce GTX Titan, Package, Tesla C2070, Tesla K40

January 16, 2017 by hgpu

Decoding with Finite-State Transducers on GPUs

Arturo Argueta, David Chiang

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Tags: Algorithms, Computer science, CUDA, Machine learning, NLP, nVidia, nVidia GeForce GTX Titan X, Package, Speech recognition, Tesla K40, Tesla P100

January 16, 2017 by hgpu

DeepDSL: A Compilation-based Domain-Specific Language for Deep Learning

Tian Zhao, Xiaobing Huang, Yu Cao

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Tags: Computer science, CUDA, Deep learning, DSL, Java, Machine learning, nVidia, Package, Programming Languages, Scala, Tesla K40

January 10, 2017 by hgpu

SeedFold: Scaling Biomolecular Structure Prediction

Tilus: A Tile-Level GPU Kernel Programming Language

Tilus: A Tile-Level GPGPU Programming Language for Low-Precision Computation

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

BoltzGen:Toward Universal Binder Design

cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

MATLAB Tensor Core models

Accurate Models of NVIDIA Tensor Cores

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

TritonForge: Profiling-Guided Framework for Automated Triton Kernel Optimization

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

tritonBLAS: A Lightweight Triton-based General Matrix Multiplication (GEMM) Library

tritonBLAS: Triton-based Analytical Approach for GEMM Kernel Parameter Selection

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high performance computing on graphics processing units: hgpu.org

Applications

gearshifft – The FFT Benchmark Suite for Heterogeneous Platforms

GraviDy: a GPU modular, parallel N-body integrator

Parallel Implementations of the Cholesky Decomposition on CPUs and GPUs

A GPU-Based Solution to Fast Calculation of Betweenness Centrality on Large Weighted Networks

DeepBach: a Steerable Model for Bach chorales generation

OpenNMT: Open-Source Toolkit for Neural Machine Translation

Xeon Phi: A comparison between the newly introduced MIC architecture and a standard CPU through three types of problems

Parallelizing Exact and Approximate String Matching via Inclusive Scan on a GPU

An N log N Parallel Fast Direct Solver for Kernel Matrices

Using efficient parallelization in Graphic Processing Units to parameterize stochastic fire propagation models

Decoding with Finite-State Transducers on GPUs

DeepDSL: A Compilation-based Domain-Specific Language for Deep Learning

Recent source codes

SeedFold: Scaling Biomolecular Structure Prediction

Tilus: A Tile-Level GPU Kernel Programming Language

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

BoltzGen:Toward Universal Binder Design

cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

MATLAB Tensor Core models

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

tritonBLAS: A Lightweight Triton-based General Matrix Multiplication (GEMM) Library

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