high performance computing on graphics processing units: hgpu.org

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Long-time Simulations with Complex Code Using Multiple Nodes of Intel Xeon Phi Knights Landing

Jonathan S. Graf, Matthias K. Gobbert, Samuel Khuvis

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Tags: Computer science, Differential equations, Intel Xeon Phi, MPI, OpenMP, Partial differential equations, PDEs

February 17, 2018 by hgpu

Tuning Streamed Applications on Intel Xeon Phi: A Machine Learning Based Approach

Peng Zhang, Jianbin Fang, Tao Tang, Canqun Yang, Zheng Wang

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Tags: Benchmarking, Computer science, Intel Xeon Phi, Machine learning, Package, Performance

February 9, 2018 by hgpu

Efficient SIMD Vectorization for Hashing in OpenCL

Tobias Behrens, Viktor Rosenfeld, Jonas Traub, Sebastian Bress, Volker Markl

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Tags: Computer science, Databases, Hashing, Intel Xeon Phi, OpenCL

February 3, 2018 by hgpu

SkePU 2: Flexible and Type-Safe Skeleton Programming for Heterogeneous Parallel Systems

August Ernstsson, Christoph Kessler

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Tags: Compilers, Computer science, CUDA, Heterogeneous systems, Intel Xeon Phi, nVidia, OpenCL, OpenMP, Package, Tesla K20

January 28, 2018 by hgpu

Revisiting Online Autotuning for Sparse-Matrix Vector Multiplication Kernels on Next-Generation Architectures

Simon Garcia De Gonzalo, Simon D. Hammond, Christian R. Trott, Wen-Mei Hwu

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Tags: Auto-Tuning, Computer science, CUDA, Intel Xeon Phi, nVidia, Sparse matrix, Tesla K20, Tesla K40, Tesla K80, Tesla P100

January 28, 2018 by hgpu

Scheduling Parallel Tasks under Multiple Resources: List Scheduling vs. Pack Scheduling

Hongyang Sun, Redouane Elghazi, Ana Gainaru, Guillaume Aupy, Padma Raghavan

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Tags: Computer science, Intel Xeon Phi, Performance, Task scheduling

January 20, 2018 by hgpu

Scaling GRPC Tensorflow on 512 nodes of Cori Supercomputer

Amrita Mathuriya, Thorsten Kurth, Vivek Rane, Mustafa Mustafa, Lei Shao, Debbie Bard, Prabhat, Victor W Lee

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Tags: Benchmarking, Computer science, Deep learning, Intel Xeon Phi, Neural networks, Performance, TensorFlow

January 6, 2018 by hgpu

Using reconfigurable computing technology to accelerate matrix decomposition and applications

Xinying Wang

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Tags: Computer science, Data mining, Factorization, FPGA, Intel Xeon Phi, Matrix decomposition, Semantic indexing, Signal processing, Text mining, Thesis

December 28, 2017 by hgpu

An In-depth Performance Characterization of CPU- and GPU-based DNN Training on Modern Architectures

Ammar Ahmad Awan, Hari Subramoni, Dhabaleswar K. Panda

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Tags: Benchmarking, Caffe, Computer science, CUBLAS, CUDA, Deep learning, Intel Xeon Phi, Machine learning, nVidia, Tela K40, Tesla K80, Tesla P100

December 24, 2017 by hgpu

POMPEI: Programming with OpenMP4 for Exascale Investigations

Jack Dongarra, Azzam Haidar, Oscar Hernandez, Stanimire Tomov, Manjunath Gorentla Venkata

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Tags: Computer science, Intel Xeon Phi, Linear Algebra, nVidia, OpenACC, OpenMP, Programming techniques, Tesla M2090

December 15, 2017 by hgpu

On algorithmic reductions in task-parallel programming models

Jan Ciesko

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Tags: Computer science, Intel Xeon Phi, OpenMP, Package, Thesis

December 10, 2017 by hgpu

Practical Implementation of Lattice QCD Simulation on Intel Xeon Phi Knights Landing

Issaku Kanamori, Hideo Matsufuru

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Tags: Computational Physics, High Energy Physics - Lattice, Intel Xeon Phi, Numerical simulation, Physics, QCD

December 7, 2017 by hgpu

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

SimSYCL: A SYCL Implementation Targeting Development, Debugging, Simulation and Conformance

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

gpu_tracker: Python package for tracking and profiling GPU utilization in both desktop and high-performance computing environments

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

94% on CIFAR-10 in 3.29 Seconds on a Single GPU

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

LOOPer: A Learned Automatic Code Optimizer For Polyhedral Compilers

OpenMC Monte Carlo Code

Performance Portable Monte Carlo Particle Transport on Intel, NVIDIA, and AMD GPUs

Polygeist: C/C++ frontend for MLIR

Retargeting and Respecializing GPU Workloads for Performance Portability

Parallel Gaussian process with kernel approximation in CUDA

See all packages

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high performance computing on graphics processing units: hgpu.org

Long-time Simulations with Complex Code Using Multiple Nodes of Intel Xeon Phi Knights Landing

Tuning Streamed Applications on Intel Xeon Phi: A Machine Learning Based Approach

Efficient SIMD Vectorization for Hashing in OpenCL

SkePU 2: Flexible and Type-Safe Skeleton Programming for Heterogeneous Parallel Systems

Revisiting Online Autotuning for Sparse-Matrix Vector Multiplication Kernels on Next-Generation Architectures

Scheduling Parallel Tasks under Multiple Resources: List Scheduling vs. Pack Scheduling

Scaling GRPC Tensorflow on 512 nodes of Cori Supercomputer

Using reconfigurable computing technology to accelerate matrix decomposition and applications

An In-depth Performance Characterization of CPU- and GPU-based DNN Training on Modern Architectures

POMPEI: Programming with OpenMP4 for Exascale Investigations

On algorithmic reductions in task-parallel programming models

Practical Implementation of Lattice QCD Simulation on Intel Xeon Phi Knights Landing

Recent source codes

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

GPU plugin for PySCF

QArray

Celerity: High-level C++ for Accelerator Clusters

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

OpenMC Monte Carlo Code

Polygeist: C/C++ frontend for MLIR

Parallel Gaussian process with kernel approximation in CUDA

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