high performance computing on graphics processing units: hgpu.org

Martin Bauer, Sebastian Eibl, Christian Godenschwager, Nils Kohl, Michael Kuron, Christoph Rettinger, Florian Schornbaum, Christoph Schwarzmeier, Dominik Thönnes, Harald Köstler, Ulrich Rüde

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Tags: Code generation, CUDA, Heterogeneous systems, Lattice Boltzmann model, MPI, nVidia, Package, Particle simulation, performance portability, Physics, Tesla P100

October 6, 2019 by hgpu

MNN: a lightweight deep neural network inference engine

An Image Enhancing Pattern-based Sparsity for Real-time Inference on Mobile Devices

SolidTextureNets

On Demand Solid Texture Synthesis Using Deep 3D Networks

md_poly: A Performance-Portable Polyhedral Compiler Based on Multi-Dimensional Homomorphisms

deepnetts: Deep Netts Pure Java Deep Learning Engine

Towards High Performance Java-based Deep Learning Frameworks

PASTA: A Parallel Sparse Tensor Algorithm Benchmark Suite

A Parallel Sparse Tensor Benchmark Suite on CPUs and GPUs

TACO: The Tensor Algebra Compiler (taco) computes sparse tensor expressions on CPUs and GPUs

A Unified Iteration Space Transformation Framework for Sparse and Dense Tensor Algebra

OpenCL-FPGA-LLVM: LLVM-based automation of memory decoupling for OpenCL applications on FPGAs

LLVM-based automation of memory decoupling for OpenCL applications on FPGAs

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Munich, Germany

8th International Workshop on OpenCL, including SYCLCon, 2019

2019

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13-15

Boston, USA

7th International Workshop on OpenCL, 2019

2019

May

Boston, MA, USA

Distributed Heterogeneous Programming in C/C++ (DHPCC++), 2019

2019

May

Larnaca, Cyprus

3rd International Workshop on Theoretical Approaches to Performance Evaluation, Modeling and Simulation (TAPEMS), 2019

2019

July

26-28

Rome, Italy

International Conference on Frontiers of Neural Networks (ICFNN), 2019

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high performance computing on graphics processing units: hgpu.org

Using Parallel Programming Models for Automotive Workloads on Heterogeneous Systems – a Case Study

Automatically Harnessing Sparse Acceleration

Towards High Performance Java-based Deep Learning Frameworks

Static Analysis and Dynamic Adaptation of Parallelism

Sparse matrix partitioning for optimizing SpMV on CPU-GPU heterogeneous platforms

Abstractions for Programming Graphics Processors in High-Level Programming Languages

Accelerating Molecular Docking by Parallelized Heterogeneous Computing – A Case Study of Performance, Quality of Results, and Energy-Efficiency using CPUs, GPUs, and FPGAs

Scalable Applications on Heterogeneous System Architectures: A Systematic Performance Analysis Framework

Compiler-Driven Performance on Heterogeneous Computing Platforms

Accelerating Stochastic Simulations on GPUs Using OpenCL

Blink: Fast and Generic Collectives for Distributed ML

waLBerla: A block-structured high-performance framework for multiphysics simulations

Recent source codes

MNN: a lightweight deep neural network inference engine

SolidTextureNets

md_poly: A Performance-Portable Polyhedral Compiler Based on Multi-Dimensional Homomorphisms

deepnetts: Deep Netts Pure Java Deep Learning Engine

PASTA: A Parallel Sparse Tensor Algorithm Benchmark Suite

TACO: The Tensor Algebra Compiler (taco) computes sparse tensor expressions on CPUs and GPUs

OpenCL-FPGA-LLVM: LLVM-based automation of memory decoupling for OpenCL applications on FPGAs

CUDAnative.jl: Julia support for native CUDA programming

GPU explicit-state model checking

OCLADock: OpenCL Accelerated Molecular Docking

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Featured events

8th International Workshop on OpenCL, including SYCLCon, 2019

7th International Workshop on OpenCL, 2019

Distributed Heterogeneous Programming in C/C++ (DHPCC++), 2019

3rd International Workshop on Theoretical Approaches to Performance Evaluation, Modeling and Simulation (TAPEMS), 2019

International Conference on Frontiers of Neural Networks (ICFNN), 2019