high performance computing on graphics processing units: hgpu.org

Applications

hgpu.org » Applications

A Hybrid Framework for Fast and Accurate GPU Performance Estimation through Source-Level Analysis and Trace-Based Simulation

Xiebing Wang, Kai Huang, Alois Knoll, Xuehai Qian

View

Tags: Computer science, nVidia, nVidia GeForce GTX 645, nVidia GeForce GTX 940 M, nVidia Quadro K600, nVidia Quadro K620, OpenCL, Performance

May 26, 2019 by hgpu

Performance Analysis of Deep Learning Workloads on Leading-edge Systems

Yihui Ren, Shinjae Yoo, Adolfy Hoisie

View

Download (PDF)

Tags: Cloud, Computer science, Computer vision, CUDA, Deep learning, Machine learning, NLP, nVidia, nVidia DGX-2, nVidia GeForce RTX 2080 Ti, Performance, Tesla V100

May 23, 2019 by hgpu

A Case Study: Exploiting Neural Machine Translation to Translate CUDA to OpenCL

Yonghae Kim, Hyesoon Kim

View

Download (PDF)

Tags: Code generation, Computer science, CUDA, Deep learning, LSTM, NLP, nVidia, OpenCL

May 23, 2019 by hgpu

Instructions’ Latencies Characterization for NVIDIA GPGPUs

Yehia Arafa, Abdel-Hameed Badawy, Gopinath Chennupati, Nandakishore Santhi, Stephan Eidenbenz

View

Download (PDF)

Tags: Benchmarking, Computer science, CUDA, nVidia, nVidia GeForce GTX Titan X, nVidia Titan RTX, Performance, PTX, Tesla K40, Tesla P100, Tesla V100

May 23, 2019 by hgpu

Neural Query Language: A Knowledge Base Query Language for Tensorflow

William W. Cohen, Matthew Siegler, Alex Hofer

View

Download (PDF)

Source codes

Tags: Computer science, Databases, Deep learning, Package, Performance

May 19, 2019 by hgpu

Optimizing the Linear Fascicle Evaluation Algorithm for Multi-Core and Many-Core Systems

Karan Aggarwal, Uday Bondhugula, Varsha Sreenivasan, Devarajan Sridharan

View

Download (PDF)

Tags: Algorithms, Computer science, CUDA, Neuroscience, nVidia, nVidia GeForce RTX 2080 Ti, Sparse matrix

May 19, 2019 by hgpu

Accelerating Deterministic and Stochastic Binarized Neural Networks on FPGAs Using OpenCL

Corey Lammie, Wei Xiang, Mostafa Rahimi Azghadi

View

Download (PDF)

Tags: Computer science, Deep learning, FPGA, Heterogeneous systems, Neural networks, OpenCL

May 19, 2019 by hgpu

OpenDNN: An Open-source, cuDNN-like Deep Learning Primitive Library

Daeyeon Kim

View

Download (PDF)

Tags: Computer science, Deep learning, FPGA, Neural networks, OpenCL, Performance, Thesis

May 19, 2019 by hgpu

CUDA au Coq: A Framework for Machine-validating GPU Assembly Programs

Benjamin Ferrell, Jun Duan, Kevin W. Hamlen

View

Download (PDF)

Tags: Algorithms, Compilers, Computer science, CUDA, nVidia, PTX

May 15, 2019 by hgpu

A Unified Approach to Variable Renaming for Enhanced Vectorization

Prasanth Chatarasi, Jun Shirako, Albert Cohen, Vivek Sarkar

View

Download (PDF)

Tags: Benchmarking, Compilers, Computer science, CUDA, Intel Xeon Phi, nVidia, Tesla V100

May 15, 2019 by hgpu

An optimizing multi-platform source-to-source compiler framework for the NEURON MODeling Language

Pramod Kumbhar, Omar Awile, Liam Keegan, Jorge Blanco Alonso, James King, Michael Hines, Felix Schuermann

View

Download (PDF)

Source codes

Tags: Biophysics, Code generation, Compilers, Computer science, CUDA, HPC, Intel Xeon Phi, LLVM, nVidia, OpenACC, OpenMP, Package

May 15, 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

* * *

high performance computing on graphics processing units: hgpu.org

Applications

A Hybrid Framework for Fast and Accurate GPU Performance Estimation through Source-Level Analysis and Trace-Based Simulation

A Case Study: Exploiting Neural Machine Translation to Translate CUDA to OpenCL

Instructions’ Latencies Characterization for NVIDIA GPGPUs

Neural Query Language: A Knowledge Base Query Language for Tensorflow

Optimizing the Linear Fascicle Evaluation Algorithm for Multi-Core and Many-Core Systems

Accelerating Deterministic and Stochastic Binarized Neural Networks on FPGAs Using OpenCL

OpenDNN: An Open-source, cuDNN-like Deep Learning Primitive Library

CUDA au Coq: A Framework for Machine-validating GPU Assembly Programs

A Unified Approach to Variable Renaming for Enhanced Vectorization

An optimizing multi-platform source-to-source compiler framework for the NEURON MODeling Language

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

Most viewed papers (last 30 days)