high performance computing on graphics processing units: hgpu.org

Tianqi Chen, Thierry Moreau, Ziheng Jiang, Lianmin Zheng, Eddie Yan, Meghan Cowan, Haichen Shen, Leyuan Wang, Yuwei Hu, Luis Ceze, Carlos Guestrin, Arvind Krishnamurthy

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Tags: Compilers, Computer science, Deep learning, FPGA, Machine learning, nVidia, nVidia GeForce GTX Titan X, Optimization, Package, performance portability

October 13, 2018 by hgpu

Evaluating Performance Portability of Accelerator Programming Models using SPEC ACCEL 1.2 Benchmarks

Swen Boehm, Swaroop Pophale, Veronica G. Vergara Larrea, Oscar Hernandez

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Tags: Benchmarking, Computer science, Heterogeneous systems, Intel Xeon Phi, nVidia, OpenACC, OpenCL, OpenMP, performance portability, Tesla K20, Tesla V100

September 23, 2018 by hgpu

Dynamic Distribution Pruning for Efficient Network Architecture Search

Tinker-HP: High Performance Massively Parallel Evolution of Tinker

Raising the Performance of the Tinker-HP Molecular Modeling Package on Intel’s HPC Architectures: a Living Review [Article v1.0]

PyVisc

Classify QCD phase transition with deep learning

Bytespresso: a library for Java to C/CUDA translator based on deep reification

A Development Platform for Embedded Domain-Specific Languages

faimGraph

Breadth-First Search using Dynamic Parallelism on the GPU

Vulkan Multi-GPU application

Multi-GPU Rendering with Vulkan API

Benchmarking Analysis of Vision Kernels on Embedded CPU, GPU and FPGA

Comparing Energy Efficiency of CPU, GPU and FPGA Implementations for Vision Kernels

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high performance computing on graphics processing units: hgpu.org

Automatic generation of warp-level primitives and atomic instructions for fast and portable parallel reduction on GPUs

Principles, Techniques, and Tools for Explicit and Automatic Parallelization

Cross-Platform Performance Portability Using Highly Parametrized SYCL Kernels

On the Portability of GPU-Accelerated Applications via Automated Source-to-Source Translation

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

Heterogeneous Distributed Big Data Clustering on Sparse Grids

Efficient Implementation and Optimization of Geometric Multigrid Operations in the LIFT Framework

Delivering Performance-Portable Stencil Computations on CPUs and GPUs Using Bricks

Performance Portability Challenges for Fortran Applications

OpenCL Performance Prediction using Architecture-Independent Features

TVM: An Automated End-to-End Optimizing Compiler for Deep Learning

Evaluating Performance Portability of Accelerator Programming Models using SPEC ACCEL 1.2 Benchmarks

Recent source codes

PSST: A Tutorial for Parallelised Scalable Simulations in TensorFlow

MUSE: Miami University Simulation Environment

Pressure Point Analysis framework

Dynamic Distribution Pruning for Efficient Network Architecture Search

Tinker-HP: High Performance Massively Parallel Evolution of Tinker

PyVisc

Bytespresso: a library for Java to C/CUDA translator based on deep reification

faimGraph

Vulkan Multi-GPU application

Benchmarking Analysis of Vision Kernels on Embedded CPU, GPU and FPGA

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Featured events

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

International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML), 2019