high performance computing on graphics processing units: hgpu.org

Programming

hgpu.org » Programming

FPGA-based acceleration of a particle simulation High Performance Computing application

Aldo Conte

View

Download (PDF)

Tags: FPGA, Molecular dynamics, OpenCL, Particle simulation, Physics, Thesis

May 8, 2019 by hgpu

Principles, Techniques, and Tools for Explicit and Automatic Parallelization

Nico Reissmann

View

Download (PDF)

Source codes

Tags: Computer science, CUDA, nVidia, OpenMP, Package, Performance, performance portability, Tesla K20, Thesis

May 5, 2019 by hgpu

Compressed Learning of Deep Neural Networks for OpenCL-Capable Embedded Systems

Sangkyun Lee, Jeonghyun Lee

View

Download (PDF)

Source codes

Tags: Algorithms, ARM, Compression, Computer science, Neural networks, nVidia, nVidia GeForce GTX 1080 Ti, OpenCL, Package

May 5, 2019 by hgpu

Full-stack Optimization for Accelerating CNNs with FPGA Validation

Bradley McDanel, Sai Qian Zhang, H. T. Kung, Xin Dong

View

Download (PDF)

Source codes

Tags: CNN, Computer science, CUDA, Deep learning, FPGA, Machine learning, Neural networks, Package

May 5, 2019 by hgpu

AdaNet: A Scalable and Flexible Framework for Automatically Learning Ensembles

Charles Weill, Javier Gonzalvo, Vitaly Kuznetsov, Scott Yang, Scott Yak, Hanna Mazzawi, Eugen Hotaj, Ghassen Jerfel, Vladimir Macko, Ben Adlam, Mehryar Mohri, Corinna Cortes

View

Download (PDF)

Source codes

Tags: Algorithms, Computer science, Machine learning, Neural networks, nVidia, Package, TensorFlow, Tesla V100

May 5, 2019 by hgpu

Transparent Acceleration for Heterogeneous Platforms With Compilation to OpenCL

Heinrich Riebler, Gavin Vaz, Tobias Kenter, Christian Plessl

View

Download (PDF)

Tags: Compilers, Computer science, Heterogeneous systems, Intel Xeon Phi, nVidia, OpenCL, Tesla K20

May 1, 2019 by hgpu

A mechanism for balancing accuracy and scope in cross-machine black-box GPU performance modeling

James D. Stevens

View

Download (PDF)

Tags: AMD Radeon R9 Fury, ATI, Code generation, Computer science, Finite difference, Heterogeneous systems, Matrix multiplication, nVidia, nVidia GeForce GTX Titan X, OpenCL, Performance, Tesla C2070, Tesla K40

April 28, 2019 by hgpu

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

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

View

Download (PDF)

Source codes

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

GPU-based Efficient Join Algorithms on Hadoop

Hongzhi Wang, Ning Li, Zheng Wang, Jianing Li

View

Download (PDF)

Tags: Algorithms, Computer science, CUDA, Databases, Hadoop, nVidia

April 28, 2019 by hgpu

Loop Perforation in OpenACC

Ahmad Lashgar, Ehsan Atoofian, Amirali Baniasadi

View

Download (PDF)

Source codes

Tags: Computer science, CUDA, OpenACC, OpenCL, Package, Performance

April 20, 2019 by hgpu

On Optimizing Complex Stencils on GPUs

Prashant Singh Rawat, Miheer Vaidya, Aravind Sukumaran-Rajam, Atanas Rountev, Louis-Noël Pouchet, P. Sadayappan

View

Download (PDF)

Tags: Code generation, Computer science, CUDA, nVidia, Stencil computation, Tesla P100

April 20, 2019 by hgpu

Real world applications of Artificial Intelligence on constrained hardware

Wiebe Van Ranst

View

Download (PDF)

Tags: Artificial intelligence, Computer science, Computer vision, Image processing, nVidia, nVidia Jetson TX2, OpenCL, Thesis

April 20, 2019 by hgpu

Tutoring LLM into a Better CUDA Optimizer

Adaptivity in AdaptiveCpp: Optimizing Performance by Leveraging Runtime Information During JIT-Compilation

Kernel Library for LLM Serving

Compiler and Runtime Systems for Generative AI Models

Neptune: Advanced ML Operator Fusion for Locality and Parallelism on GPUs

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

Programming

FPGA-based acceleration of a particle simulation High Performance Computing application

Principles, Techniques, and Tools for Explicit and Automatic Parallelization

Compressed Learning of Deep Neural Networks for OpenCL-Capable Embedded Systems

Full-stack Optimization for Accelerating CNNs with FPGA Validation

AdaNet: A Scalable and Flexible Framework for Automatically Learning Ensembles

Transparent Acceleration for Heterogeneous Platforms With Compilation to OpenCL

A mechanism for balancing accuracy and scope in cross-machine black-box GPU performance modeling

GPU-based Efficient Join Algorithms on Hadoop

Loop Perforation in OpenACC

On Optimizing Complex Stencils on GPUs

Real world applications of Artificial Intelligence on constrained hardware

Recent source codes

OpScanner

Atlas CLI: Machine Learning (ML) Lifecycle & Transparency Manager

transformers_tvm: Implementation of Encoder Decoder transformer on TVM

INT v.s. FP: A framework to compare low-bit integer and float-point formats

AutoDock-GPU: AutoDock for GPUs and other accelerators

NCCLX: collective communication framework

Tutoring LLM into a Better CUDA Optimizer

Adaptivity in AdaptiveCpp: Optimizing Performance by Leveraging Runtime Information During JIT-Compilation

Kernel Library for LLM Serving

Neptune: Advanced ML Operator Fusion for Locality and Parallelism on GPUs

Most viewed papers (last 30 days)