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Synkhronos: a Multi-GPU Theano Extension for Data Parallelism

Adam Stooke, Pieter Abbeel

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Tags: Computer science, CUDA, Data parallelism, Deep learning, nVidia, nVidia DGX-1, Package, Tesla P100, Theano

October 15, 2017 by hgpu

SoAx: A generic C++ Structure of Arrays for handling Particles in HPC Codes

Holger Homann, Francois Laenen

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Tags: Benchmarking, Computational Physics, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GT 755 M, Package, Performance, Physics, Tesla M2050

October 15, 2017 by hgpu

Energy efficiency of finite difference algorithms on multicore CPUs, GPUs, and Intel Xeon Phi processors

Satya P. Jammy, Christian T. Jacobs, David J. Lusher, Neil D. Sandham

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Tags: Algorithms, Computer science, CUDA, Energy-efficient computing, Finite difference, Intel Xeon Phi, nVidia, Tesla K40

October 3, 2017 by hgpu

Performance Evaluation of Container-based Virtualization for High Performance Computing Environments

Carlos Arango, Remy Dernat, John Sanabria

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Tags: Benchmarking, Computer science, CUDA, MPI, nVidia, Operating systems, Package, Performance, Tesla K20, Virtualization

October 3, 2017 by hgpu

GALARIO: a GPU Accelerated Library for Analysing Radio Interferometer Observations

Marco Tazzari, Frederik Beaujean, Leonardo Testi

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Tags: Astrophysics, CUDA, Instrumentation and Methods for Astrophysics, nVidia, nVidia GeForce GTX 1060, Package, Python, Tesla P100

September 28, 2017 by hgpu

Modeling the Resource Requirements of Convolutional Neural Networks on Mobile Devices

Zongqing Lu, Swati Rallapalli, Kevin Chan, Thomas La Porta

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Tags: Computer science, Computer vision, CUDA, Deep learning, Neural networks, nVidia, nVidia Jetson TK1, nVidia Tegra TX1

September 28, 2017 by hgpu

IBM Deep Learning Service

Bishwaranjan Bhattacharjee, Scott Boag, Chandani Doshi, Parijat Dube, Ben Herta, Vatche Ishakian, K. R. Jayaram, Rania Khalaf, Avesh Krishna, Yu Bo Li, Vinod Muthusamy, Ruchir Puri, Yufei Ren, Florian Rosenberg, Seetharami R. Seelam, Yandong Wang, Jian Ming Zhang, Li Zhang

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Tags: Cloud, Computer science, CUDA, Deep learning, Heterogeneous systems, Machine learning, Neural networks, nVidia

September 21, 2017 by hgpu

Distributed Training Large-Scale Deep Architectures

Shang-Xuan Zou, Chun-Yen Chen, Jui-Lin Wu, Chun-Nan Chou, Chia-Chin Tsao, Kuan-Chieh Tung, Ting-Wei Lin, Cheng-Lung Sung, Edward Y. Chang

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Tags: Algorithms, Benchmarking, Computer science, CUDA, Data parallelism, Deep learning, nVidia, Tesla K80

September 21, 2017 by hgpu

Out-of-core Implementation for Accelerator Kernels on Heterogeneous Clouds

Hamidreza Khaleghzadeh, Ziming Zhong, Ravi Reddy, Alexey Lastovetsky

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Tags: BLAS, Cloud, Computer science, CUBLAS, CUDA, FPGA, Heterogeneous systems, Intel Xeon Phi, Matrix multiplication, nVidia, OpenCL, Package, Virtualization

September 16, 2017 by hgpu

Monte Carlo methods for massively parallel computers

Martin Weigel

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Tags: CUDA, Monte Carlo simulation, nVidia, nVidia GeForce GTX Titan X, Phase transition, Physics, Spin Systems, Tesla C1060, Tesla K20

September 16, 2017 by hgpu

Meta Networks for Neural Style Transfer

Falong Shen, Shuicheng Yan, Gang Zeng

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Tags: Caffe, Computer science, CUDA, Deep learning, nVidia, nVidia GeForce GTX Titan X, Package

September 16, 2017 by hgpu

GPU-Accelerated Parallel Finite-Difference Time-Domain Method for Electromagnetic Waves Propagation in Unmagnetized Plasma Media

Xi-min Wang, Lang-lang Xiong, Song Liu, Zhi-yun Peng, Shuang-ying Zhong

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Tags: Computational Physics, CUDA, FDTD, Finite-difference time-domain, nVidia, nVidia GeForce GT 650 M, Physics, Plasma physics

September 12, 2017 by hgpu

CuPBoP-AMD: Extending CUDA to AMD Platforms

CuPBoP: Making CUDA a Portable Language

Adopter: Automated Deep Learning Optimization via DSL-based Source Code Transformation

Automated Deep Learning Optimization via DSL-Based Source Code Transformation

ROCm's implementation of Gromacs

GROMACS on AMD GPU-Based HPC Platforms: Using SYCL for Performance and Portability

Code examples for paper on SYCL backend of Kokkos - IWOCL 2024

Experiences with implementing Kokkos’ SYCL backend

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

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

GPU plugin for PySCF

Python-Based Quantum Chemistry Calculations with GPU Acceleration

QArray

QArray: a GPU-accelerated constant capacitance model simulator for large quantum dot arrays

Celerity: High-level C++ for Accelerator Clusters

Balancing Tracking Granularity and Parallelism in Many-Task Systems: The Horizons Approach

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

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