high performance computing on graphics processing units: hgpu.org

Tommaso Biagini, Giovanni Chillemi, Gianluigi Mazzoccoli, Alessandro Grottesi, Caterina Fusilli, Daniele Capocefalo, Stefano Castellana, Angelo Luigi Vescovi, Tommaso Mazza

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Tags: Benchmarking, Bioinformatics, Biology, Chemistry, CUDA, Genomics, Molecular dynamics, nVidia, OpenCL, Physics, Tesla C2070

December 19, 2017 by hgpu

Effective Extensible Programming: Unleashing Julia on GPUs

Tim Besard, Christophe Foket, Bjorn De Sutter

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Tags: Code generation, Computer science, CUDA, High-level Languages, LLVM, nVidia, nVidia GeForce GTX 1080, Package, Programming Languages

December 15, 2017 by hgpu

Intra-node Memory Safe GPU Co-Scheduling

Carlos Reano, Federico Silla, Dimitrios S. Nikolopoulos, Blesson Varghese

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Tags: Computer science, CUDA, nVidia, Package, Task scheduling, Tesla K20

December 15, 2017 by hgpu

Distributed learning of CNNs on heterogeneous CPU/GPU architectures

Jose Marques, Gabriel Falcao, Luis A. Alexandre

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Tags: ATI, ATI Radeon HD 7500 M, CNN, Computer science, CUDA, Deep learning, Heterogeneous systems, Matlab, Neural networks, nVidia, nVidia GeForce GTX 840 M, nVidia GeForce GTX 940 M, nVidia GeForce GTX 950 M, OpenCL, Tesla K20

December 10, 2017 by hgpu

A tutorial on the implementations of linear image filters in CPU and GPU

Alvaro Pardo

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GT 430, Signal processing, Tutorial

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

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high performance computing on graphics processing units: hgpu.org

ThunderSVM: A Fast SVM Library on GPUs and CPUs

Analysing the Performance of GPU Hash Tables for State Space Exploration

A Generic Inverted Index Framework for Similarity Search on the GPU

Protecting Real-Time GPU Applications on Integrated CPU-GPU SoC Platforms

An MPI-Based Python Framework for Distributed Training with Keras

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

GAMER-2: a GPU-accelerated adaptive mesh refinement code — accuracy, performance, and scalability

Molecular dynamics recipes for genome research

Effective Extensible Programming: Unleashing Julia on GPUs

Intra-node Memory Safe GPU Co-Scheduling

Distributed learning of CNNs on heterogeneous CPU/GPU architectures

A tutorial on the implementations of linear image filters in CPU and GPU

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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