high performance computing on graphics processing units: hgpu.org

Applications

hgpu.org » Applications

Custom Code Generation for a Graph DSL

Bikash Gogoi, Unnikrishnan Cheramangalath, Rupesh Nasre

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Tags: Code generation, Computer science, CUDA, DSL, nVidia, Tesla K40

March 10, 2019 by hgpu

GraphVite: A High-Performance CPU-GPU Hybrid System for Node Embedding

Zhaocheng Zhu, Shizhen Xu, Meng Qu, Jian Tang

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Tags: Computer science, Graph theory, Machine learning, nVidia, nVidia GeForce GTX 1080, Tesla P100

March 10, 2019 by hgpu

Cooperative CPU, GPU, and FPGA heterogeneous execution with EngineCL

Maria Angelica Davila Guzman, Raul Nozal, Ruben Gran Tejero, Maria Villarroya-Gaudo, Dario Suarez Gracia, Jose Luis Bosque

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Tags: Computer science, FPGA, Heterogeneous systems, load balancing, nVidia, nVidia GeForce GTX Titan X, OpenCL, Package

March 3, 2019 by hgpu

Using Compiler Directives for Performance Portability in Scientific Computing: Kernels from Molecular Simulation

Ada Sedova, Andreas Tillack, Arnold Tharrington

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Tags: Chemistry, Molecular dynamics, Molecular simulation, nVidia, OpenACC, performance portability, Physics, Tesla K20

March 3, 2019 by hgpu

Application level energy measurements and models for hybrid platform with accelerators

Kenneth O'Brien

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Tags: Computer science, Energy-efficient computing, Intel Xeon Phi, OpenCL, Tesla K40, Thesis

March 3, 2019 by hgpu

Stateful Dataflow Multigraphs: A Data-Centric Model for High-Performance Parallel Programs

Tal Ben-Nun, Johannes de Fine Licht, Alexandros Nikolaos Ziogas, Timo Schneider, Torsten Hoefler

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Tags: Computer science, CUDA, FPGA, nVidia, OpenCL, Optimization, Performance, Tesla P100

March 3, 2019 by hgpu

cuSten – CUDA Finite Difference and Stencil Library

Andrew Gloster, Lennon O'Naraigh

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Tags: Computational Physics, Computer science, CUDA, Finite difference, MPI, nVidia, nVidia GeForce GTX Titan X, Package, PDEs

March 3, 2019 by hgpu

An Empirically Guided Optimization Framework for FPGA OpenCL

Ahmed Sanaullah, Rushi Patel, Martin C. Herbordt

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Tags: Computer science, FPGA, OpenCL, Optimization

February 24, 2019 by hgpu

Optimizing Network Performance for Distributed DNN Training on GPU Clusters: ImageNet/AlexNet Training in 1.5 Minutes

Peng Sun, Wansen Feng, Ruobing Han, Shengen Yan, Yonggang Wen

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Tags: Computer science, CUDA, Deep learning, GPU cluster, Neural networks, nVidia, OpenMPI, Performance, Tesla P100, Tesla V100

February 24, 2019 by hgpu

A Package for Multi-Dimensional Monte Carlo Integration on Multi-GPUs

Hong-zhong Wu, Junjie Zhang, Long-gang Pang, Qun Wang

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Tags: Computational Physics, Monte Carlo integration, nVidia, Package, Physics, Python, TensorFlow, Tesla K40

February 24, 2019 by hgpu

Worst-Case Execution Time Guarantees for Runtime-Reconfigurable Architectures

Marvin Damschen

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Tags: Computer science, OpenCL, Performance, Thesis, Timing analysis

February 24, 2019 by hgpu

DNNVM: End-to-End Compiler Leveraging Heterogeneous Optimizations on FPGA-based CNN Accelerators

Yu Xing, Shuang Liang, Lingzhi Sui, Xijie Jia, Jiantao Qiu, Xin Liu, Yushun Wang, Yu Wang, Yi Shan

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Tags: Artificial intelligence, Caffe, Compilers, Computer science, Deep learning, FPGA, Neural networks, TensorFlow

February 24, 2019 by hgpu

Tutoring LLM into a Better CUDA Optimizer

Adaptivity in AdaptiveCpp: Optimizing Performance by Leveraging Runtime Information During JIT-Compilation

Kernel Library for LLM Serving

Compiler and Runtime Systems for Generative AI Models

Neptune: Advanced ML Operator Fusion for Locality and Parallelism on GPUs

See all packages

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high performance computing on graphics processing units: hgpu.org

Applications

Custom Code Generation for a Graph DSL

GraphVite: A High-Performance CPU-GPU Hybrid System for Node Embedding

Cooperative CPU, GPU, and FPGA heterogeneous execution with EngineCL

Using Compiler Directives for Performance Portability in Scientific Computing: Kernels from Molecular Simulation

Application level energy measurements and models for hybrid platform with accelerators

Stateful Dataflow Multigraphs: A Data-Centric Model for High-Performance Parallel Programs

cuSten – CUDA Finite Difference and Stencil Library

An Empirically Guided Optimization Framework for FPGA OpenCL

Optimizing Network Performance for Distributed DNN Training on GPU Clusters: ImageNet/AlexNet Training in 1.5 Minutes

A Package for Multi-Dimensional Monte Carlo Integration on Multi-GPUs

Worst-Case Execution Time Guarantees for Runtime-Reconfigurable Architectures

DNNVM: End-to-End Compiler Leveraging Heterogeneous Optimizations on FPGA-based CNN Accelerators

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Tutoring LLM into a Better CUDA Optimizer

Adaptivity in AdaptiveCpp: Optimizing Performance by Leveraging Runtime Information During JIT-Compilation

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