high performance computing on graphics processing units: hgpu.org

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One-shot tuner for deep learning compilers

Jaehun Ryu, Eunhyeok Park, Hyojin Sung

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Tags: Compilers, Computer science, CUDA, Deep learning, nVidia, nVidia GeForce GTX 2080 Ti, Package

March 27, 2022 by hgpu

PoCL-R: A Scalable Low Latency Distributed OpenCL Runtime

Jan Solanti, Michal Babej, Julius Ikkala, Vinod Kumar Malamal Vadakital, Pekka Jääskeläinen

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Tags: Computer science, GPU cluster, Heterogeneous systems, Matrix multiplication, nVidia, nVidia GeForce GTX 1060, nVidia GeForce GTX 2080 Ti, OpenCL, Package, Rendering, Tesla P100, Tesla V100

August 8, 2021 by hgpu

Unsupervised Deep Learning of Incompressible Fluid Dynamics

Nils Wandel, Michael Weinmann, Reinhard Klein

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Tags: Deep learning, Differential equations, Fluid dynamics, Fluid simulation, Neural networks, nVidia, nVidia GeForce GTX 2080 Ti, Partial differential equations, PDEs, PyTorch

June 21, 2020 by hgpu

OpenABLext: An automatic code generation framework for agent-based simulations on CPU-GPU-FPGA heterogeneous platforms

Jiajian Xiao, Philipp Andelfinger, Wentong Cai, Paul Richmond, Alois Knoll, David Eckhoff

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Tags: Code generation, Computer science, FPGA, Heterogeneous systems, nVidia, nVidia GeForce GTX 1060, nVidia GeForce GTX 2080 Ti, OpenCL, Package

June 7, 2020 by hgpu

Compositional Deep Learning in Futhark

Duc Minh Tran, Troels Henriksen, Martin Elsman

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Tags: Computer science, CUDA, Deep learning, nVidia, nVidia GeForce GTX 2080 Ti, Package, TensorFlow

September 1, 2019 by hgpu

The Impact of GPU DVFS on the Energy and Performance of Deep Learning: an Empirical Study

Zhenheng Tang, Yuxin Wang, Qiang Wang, Xiaowen Chu

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Tags: Computer science, Computer vision, CUDA, Deep learning, Neural networks, nVidia, nVidia GeForce GTX 2080 Ti, Performance, Tesla P100, Tesla V100

May 30, 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

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high performance computing on graphics processing units: hgpu.org

One-shot tuner for deep learning compilers

PoCL-R: A Scalable Low Latency Distributed OpenCL Runtime

Unsupervised Deep Learning of Incompressible Fluid Dynamics

OpenABLext: An automatic code generation framework for agent-based simulations on CPU-GPU-FPGA heterogeneous platforms

Compositional Deep Learning in Futhark

The Impact of GPU DVFS on the Energy and Performance of Deep Learning: an Empirical Study

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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