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Heterogeneous Active Messages (HAM) – Implementing Lightweight Remote Procedure Calls in C++

Matthias Noack

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Tags: Computer science, Heterogeneous systems, Intel Xeon Phi, Package

June 27, 2019 by hgpu

Performance Modelling of Deep Learning on Intel Many Integrated Core Architectures

Andre Viebke, Sabri Pllana, Suejb Memeti, Joanna Kolodziej

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

June 12, 2019 by hgpu

A Survey on Evaluating and Optimizing Performance of Intel Xeon Phi

Sparsh Mittal, Osho Anand, Visnu P Kumarr

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Tags: Compilers, Computer science, Intel Xeon Phi, OpenMP, survey

June 9, 2019 by hgpu

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

Luc-Henri Jolly, Alejandro Duran, Louis Lagardere, Jay W. Ponder, Pengyu Ren, Jean-Philip Piquemal

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Tags: Chemical Physics, Computational Physics, Computer science, Intel Xeon Phi, Mathematical Software, Molecular dynamics, Molecular modeling, OpenMP, Package

June 5, 2019 by hgpu

A Unified Approach to Variable Renaming for Enhanced Vectorization

Prasanth Chatarasi, Jun Shirako, Albert Cohen, Vivek Sarkar

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Tags: Benchmarking, Compilers, Computer science, CUDA, Intel Xeon Phi, nVidia, Tesla V100

May 15, 2019 by hgpu

An optimizing multi-platform source-to-source compiler framework for the NEURON MODeling Language

Pramod Kumbhar, Omar Awile, Liam Keegan, Jorge Blanco Alonso, James King, Michael Hines, Felix Schuermann

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Tags: Biophysics, Code generation, Compilers, Computer science, CUDA, HPC, Intel Xeon Phi, LLVM, nVidia, OpenACC, OpenMP, Package

May 15, 2019 by hgpu

Transparent Acceleration for Heterogeneous Platforms With Compilation to OpenCL

Heinrich Riebler, Gavin Vaz, Tobias Kenter, Christian Plessl

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

May 1, 2019 by hgpu

HeteroMap: A Runtime Performance Predictor for Efficient Processing of Graph Analytics on Heterogeneous Multi-Accelerators

Masab Ahmad, Halit Dogan, Christopher J. Michael, Omer Khan

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Tags: Algorithms, Computer science, Heterogeneous systems, Intel Xeon Phi, nVidia, nVidia GeForce GTX 750 Ti, nVidia GeForce GTX 970, OpenCL, Package, Performance

March 31, 2019 by hgpu

Energy Efficient Parallel K-Means Clustering for an Intel Hybrid Multi-Chip Package

Matheus Souza, Lucas Maciel, Pedro Henrique Penna, Henrique Freitas

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Tags: Clustering, Computer science, Energy-efficient computing, FPGA, Intel Xeon Phi, Machine learning, OpenCL

March 10, 2019 by hgpu

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

Paul Sathre, Mark Gardner, Wu-chun Feng

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Tags: AMD FirePro S9150, ATI, Computer science, CUDA, FPGA, Intel Xeon Phi, nVidia, OpenCL, Package, performance portability, Tesla K80

March 10, 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

AXC: A new format to perform the SpMV oriented to Intel Xeon Phi architecture in OpenCL

E. Coronado-Barrientos, G. Indalecio, A. Garcia-Loureiro

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Tags: Algorithms, Computer science, Intel Xeon Phi, OpenCL, Sparse matrix

February 10, 2019 by hgpu

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

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

Optical flow algorithms for SYCL

SYCL in the edge: performance and energy evaluation for heterogeneous acceleration

OpenMP5-Offload-OpenMC-Intel-PVC

Distributed OpenMP Offloading of OpenMC on Intel GPU MAX Accelerators

See all packages

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high performance computing on graphics processing units: hgpu.org

Heterogeneous Active Messages (HAM) – Implementing Lightweight Remote Procedure Calls in C++

Performance Modelling of Deep Learning on Intel Many Integrated Core Architectures

A Survey on Evaluating and Optimizing Performance of Intel Xeon Phi

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

A Unified Approach to Variable Renaming for Enhanced Vectorization

An optimizing multi-platform source-to-source compiler framework for the NEURON MODeling Language

Transparent Acceleration for Heterogeneous Platforms With Compilation to OpenCL

HeteroMap: A Runtime Performance Predictor for Efficient Processing of Graph Analytics on Heterogeneous Multi-Accelerators

Energy Efficient Parallel K-Means Clustering for an Intel Hybrid Multi-Chip Package

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

Application level energy measurements and models for hybrid platform with accelerators

AXC: A new format to perform the SpMV oriented to Intel Xeon Phi architecture in OpenCL

Recent source codes

QArray

Celerity: High-level C++ for Accelerator Clusters

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

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

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

Optical flow algorithms for SYCL

OpenMP5-Offload-OpenMC-Intel-PVC

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