high performance computing on graphics processing units: hgpu.org

Kshiteej Mahajan, Arjun Singhvi, Arjun Balasubramanian, Varun Batra, Surya Teja Chavali, Shivaram Venkataraman, Aditya Akella, Amar Phanishayee, Shuchi Chawla

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Tags: Computer science, GPU cluster, Machine learning, nVidia, Task scheduling, Tesla K80, Tesla M60

July 4, 2019 by hgpu

Predictable GPGPU Computing in DNN-Driven Autonomous Systems

Husheng Zhou

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Tags: Computer science, CUDA, Deep learning, Heterogeneous systems, Neural networks, nVidia, nVidia GeForce GTX 480, nVidia GeForce GTX 620, nVidia GeForce GTX 660, nVidia Jetson TX2, nVidia Quadro 6000, Tesla K80, Thesis

May 12, 2019 by hgpu

Chunkflow: Distributed Hybrid Cloud Processing of Large 3D Images by Convolutional Nets

Jingpeng Wu, William M. Silversmith, H. Sebastian Seung

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Tags: Cloud, CNN, Computer science, CUDA, Deep learning, Electron microscopy, Image processing, Microscopy, nVidia, nVidia GeForce GTX 970, Package, Tesla K80

April 28, 2019 by hgpu

TensorFlow Doing HPC

Steven W. D. Chien, Stefano Markidis, Vyacheslav Olshevsky, Yaroslav Bulatov, Erwin Laure, Jeffrey S. Vetter

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Tags: Benchmarking, Computer science, CUDA, Deep learning, FFT, Heterogeneous systems, HPC, Machine learning, nVidia, nVidia Quadro K420, OpenMPI, Package, Performance, Python, TensorFlow, Tesla K80, Tesla V100

March 17, 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

Performance Evaluation of OpenMP’s Target Construct on GPUs: Exploring Compiler Optimizations

Akihiro Hayashi, Jun Shirako, Etorre Tiotto, Robert Ho, Vivek Sarkar

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Tags: Compilers, Computer science, CUDA, LLVM, nVidia, OpenMP, Optimization, Tesla K80, Tesla P100

February 10, 2019 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

Automated Partitioning of Data-Parallel Kernels using Polyhedral Compilation

Understanding GPU-Based Lossy Compression for Extreme-Scale Cosmological Simulations

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

Parallelizing Multiple Flow Accumulation Algorithm using CUDA and OpenACC

Automated Architecture Design for Deep Neural Networks

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

Themis: Fair and Efficient GPU Cluster Scheduling for Machine Learning Workloads

Predictable GPGPU Computing in DNN-Driven Autonomous Systems

TensorFlow Doing HPC

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

Performance Evaluation of OpenMP’s Target Construct on GPUs: Exploring Compiler Optimizations

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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