high performance computing on graphics processing units: hgpu.org

Programming

hgpu.org » Programming

Block-Size Independence for GPU Programs

Rajeev Alur, Joseph Devietti, Nimit Singhania

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX Titan X, Performance

July 28, 2018 by hgpu

cuPentBatch – A batched pentadiagonal solver for NVIDIA GPUs

Andrew Gloster, Lennon O Naraigh, Khang Ee Pang

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Tags: Computational Physics, CUDA, Differential equations, nVidia, nVidia GeForce GTX Titan X, Package, Partial differential equations, PDEs, Physics

July 21, 2018 by hgpu

Abelian: A Compiler for Graph Analytics on Distributed, Heterogeneous Platforms

Gurbinder Gill, Roshan Dathathri, Loc Hoang, Andrew Lenharth, Keshav Pingali

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Tags: Computer science, CUDA, Graph theory, Intel Xeon Phi, nVidia, Tesla K80

July 21, 2018 by hgpu

ARC: Adaptive Ray-tracing with CUDA, a New Ray Tracing Code for Parallel GPUs

Blake Hartley, Massimo Ricotti

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Tags: Astrophysics, Cosmology, CUDA, MPI, nVidia, Raytracing

July 21, 2018 by hgpu

Cross-Compiling Shading Languages

Lukas Hermanns

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Tags: 3D Graphics and Realism, Algorithms, DirectX, GLSL, HLSL, OpenGL, Real-time graphics, Rendering, Thesis

July 15, 2018 by hgpu

Multicore architecture and cache optimization techniques for solving graph problems

Alvaro Tzul

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Tags: Computer science, CUDA, Graph theory, nVidia

July 15, 2018 by hgpu

CloudCL: Single-Paradigm Distributed Heterogeneous Computing for Cloud Infrastructures

Max Plauth, Florian Roesler, Andreas Polze

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Tags: Cloud, Computer science, Java, MPI, nVidia, nVidia GRID K520, OpenCL, Package

July 15, 2018 by hgpu

Data-Parallel Hashing Techniques for GPU Architectures

Brenton Lessley

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Tags: Computer science, CUDA, Hashing, nVidia

July 15, 2018 by hgpu

Application of Deep-Learning to Compiler-Based Graphs

Tristan Vanderbruggen

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Tags: Compilers, Computer science, Deep learning, Graph theory, Machine learning, Neural networks, nVidia, nVidia GRID K520, OpenCL, Thesis

July 7, 2018 by hgpu

Energy Consumption of Algorithms for Solving the Compressible Navier-Stokes Equations on CPU’s, GPU’s and KNL’s

Satya P. Jammy, Christian T. Jacobs, David J. Lusher, Neil D. Sandham

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Tags: CUDA, Finite difference, Fluid dynamics, Intel Xeon Phi, Navier-Stokes equations, NSEs, nVidia, Tesla K40

July 7, 2018 by hgpu

FluidFFT: common API (C++ and Python) for Fast Fourier Transform HPC libraries

Ashwin Vishnu Mohanan, Cyrille Bonamy, Pierre Augier

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Tags: Benchmarking, cfd, CUDA, FFT, Fluid dynamics, MPI, Package, Python

July 7, 2018 by hgpu

An Efficient Dispatcher for Large Scale GraphProcessing on OpenCL-based FPGAs

Chengbo Yang

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Tags: Computer science, FPGA, Graph theory, OpenCL, Task scheduling

July 7, 2018 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

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high performance computing on graphics processing units: hgpu.org

Programming

Block-Size Independence for GPU Programs

cuPentBatch – A batched pentadiagonal solver for NVIDIA GPUs

Abelian: A Compiler for Graph Analytics on Distributed, Heterogeneous Platforms

ARC: Adaptive Ray-tracing with CUDA, a New Ray Tracing Code for Parallel GPUs

Cross-Compiling Shading Languages

Multicore architecture and cache optimization techniques for solving graph problems

Data-Parallel Hashing Techniques for GPU Architectures

Application of Deep-Learning to Compiler-Based Graphs

Energy Consumption of Algorithms for Solving the Compressible Navier-Stokes Equations on CPU’s, GPU’s and KNL’s

FluidFFT: common API (C++ and Python) for Fast Fourier Transform HPC libraries

An Efficient Dispatcher for Large Scale GraphProcessing on OpenCL-based FPGAs

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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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