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GPU acceleration of preconditioned solvers for ill-conditioned linear systems

Rohit Gupta

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Tags: Computer science, Conjugate gradient solver, MPI, nVidia, Tesla C2070, Thesis

October 11, 2015 by hgpu

Automatic Online Tuning (AutoTune): Fully Extended Analysis

Eduardo Cesar, Robert Mijacovic, Carmen Navarrete, Carla Guillien, Siegfried Benkner, Martin Sandrieser, Enes Bajrovic, Laurent Morin, Gertvjola Saveta, Anna Sikora

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Tags: Computer science, MPI, OpenCL, Performance

September 19, 2015 by hgpu

Assessing the hardness of SVP algorithms in the presence of CPUs and GPUs

Fabio Jose Goncalves Correia

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Tags: Algorithms, Computer science, CUDA, MPI, nVidia, Quantum computing, Security, Thesis, Voronoi diagram

September 8, 2015 by hgpu

GHOST: Building blocks for high performance sparse linear algebra on heterogeneous systems

Moritz Kreutzer, Jonas Thies, Melven Rohrig-Zollner, Andreas Pieper, Faisal Shahzad, Martin Galgon, Achim Basermann, Holger Fehske, Georg Hager, Gerhard Wellein

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Tags: Computer science, CUDA, Heterogeneous systems, Intel Xeon Phi, Linear Algebra, MPI, nVidia, OpenMP, Package, Sparse matrix, Tesla K20

July 30, 2015 by hgpu

An algebraic parallel treecode in arbitrary dimensions

William B. March, Bo Xiao, Chenhan D. Yu, George Biros

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Tags: Algorithms, Computer science, CUDA, GPU cluster, Linear Algebra, MPI, nVidia, Tesla K40

July 22, 2015 by hgpu

Performance Analysis of GPU-Accelerated Filter-Based Source Finding for HI Spectral Line Image Data

Stefan Westerlund, Christopher Harris

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Tags: Astrophysics, CUDA, Filtering, Instrumentation and Methods for Astrophysics, MPI, nVidia, OpenCL, Programming techniques, Tesla C2075

July 20, 2015 by hgpu

PLB-HeC: A Profile-based Load-Balancing algorithm for Heterogeneous CPU-GPU Clusters

Luis Sant'Ana, Raphael Camargo, Daniel Cordeiro

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Tags: Algorithms, Computer science, CUDA, GPU cluster, Heterogeneous systems, Linear Algebra, MPI, nVidia, nVidia GeForce GTX 295, nVidia GeForce GTX 680, nVidia GeForce GTX Titan, Tesla K20

July 13, 2015 by hgpu

Towards Good Practices for Very Deep Two-Stream ConvNets

Limin Wang, Yuanjun Xiong, Zhe Wang, Yu Qiao

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Tags: Computer science, Computer vision, CUDA, Deep learning, Machine learning, MPI, nVidia, OpenMPI, Package

July 10, 2015 by hgpu

Autotuning OpenACC Work Distribution via Direct Search

Calvin Montgomery, Jeffrey L. Overbey, Xuechao Li

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Tags: Computer science, MPI, nVidia, OpenACC, Package, Performance, Python, Tesla K20

July 8, 2015 by hgpu

Experiments on Parallel Training of Deep Neural Network using Model Averaging

Hang Su, Haoyu Chen

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Tags: Computer science, CUDA, Deep learning, MPI, Neural networks, nVidia, Tesla K20

July 8, 2015 by hgpu

Best bang for your buck: GPU nodes for GROMACS biomolecular simulations

Carsten Kutzner, Szilard Pall, Martin Fechner, Ansgar Esztermann, Bert L. de Groot, Helmut Grubmuller

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Tags: Benchmarking, Biological Physics, Biomolecules, Computational Physics, Computer science, Molecular dynamics, Molecular simulation, MPI, nVidia, Package, Performance, Tesla K20, Tesla K40

July 6, 2015 by hgpu

AccFFT: A library for distributed-memory FFT on CPU and GPU architectures

Amir Gholami, Judith Hill, Dhairya Malhotra, George Biros

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Tags: Algorithms, Computer science, CUDA, FFT, MPI, nVidia, OpemMP, Package, Tesla K20

June 30, 2015 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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