high performance computing on graphics processing units: hgpu.org

hgpu.org » Applications » Computer science

Supporting mixed-datatype matrix multiplication within the BLIS framework

Field G. Van Zee, Devangi N. Parikh, Robert A. van de Geijn

View

Download (PDF)

Source codes

Tags: Computer science, Matrix multiplication, OpenMP, Package

January 27, 2019 by hgpu

Efficient Implementation and Optimization of Geometric Multigrid Operations in the LIFT Framework

Martin Lucke

View

Download (PDF)

Tags: Computer science, Differential equations, nVidia, nVidia GeForce GTX 1080, OpenCL, Partial differential equations, PDEs, performance portability, Thesis

January 27, 2019 by hgpu

AccUDNN: A GPU Memory Efficient Accelerator for Training Ultra-deep Deep Neural Networks

Jinrong Guo, Wantao Liu, Wang Wang, Qu Lu, Songlin Hu, Jizhong Han, Ruixuan Li

View

Download (PDF)

Tags: Computer science, CUDA, Deep learning, Neural networks, nVidia, Tesla M40

January 27, 2019 by hgpu

Exploring FPGA-specific Optimizations for Irregular OpenCL Applications

Mohamed W. Hassan, Ahmed E. Helal, Peter M. Athanas, Wu-Chun Feng, Yasser Y. Hanafy

View

Download (PDF)

Source codes

Tags: Benchmarking, Computer science, FPGA, HLS, Linear Algebra, OpenCL, Package

January 20, 2019 by hgpu

AutoPhase: Compiler Phase-Ordering for High Level Synthesis with Deep Reinforcement Learning

Ameer Haj-Ali, Qijing Huang, William Moses, John Xiang, Ion Stoica, Krste Asanovic, John Wawrzynek

View

Download (PDF)

Tags: Algorithms, Code generation, Compilers, Computer science, Deep learning, HLS, LLVM, Programming Languages

January 20, 2019 by hgpu

Automatic acceleration of Numpy applications on GPUs and multicore CPUs

Mahesh Ravishankar, Vinod Grover

View

Download (PDF)

Tags: Computer science, nVidia, Programming Languages

January 20, 2019 by hgpu

A Survey of FPGA Based Deep Learning Accelerators: Challenges and Opportunities

Teng Wang, Chao Wang, Xuehai Zhou, Huaping Chen

View

Download (PDF)

Tags: Computer science, Deep learning, FPGA, Neural networks, Review

January 20, 2019 by hgpu

Tango: A Deep Neural Network Benchmark Suite for Various Accelerators

Aajna Karki, Chethan Palangotu Keshava, Spoorthi Mysore Shivakumar, Joshua Skow, Goutam Madhukeshwar Hegde, Hyeran Jeon

View

Download (PDF)

Source codes

Tags: Benchmarking, Computer science, CUDA, Deep learning, FPGA, Machine learning, Neural networks, nVidia, nVidia Jetson TX1, OpenCL, Package

January 20, 2019 by hgpu

Vulkan 1.1.97 – A Specification (with all registered Vulkan extensions)

The Khronos Vulkan Working Group

View

Download (PDF)

Tags: Computer science, Vulkan, Whitepaper

January 13, 2019 by hgpu

Auto-tuned OpenCL kernel co-execution in OmpSs for heterogeneous systems

B. P'erez, E. Stafford, J. L. Bosque, R. Beivide, S. Mateo, X. Teruel, X. Martorell, E. Ayguad'e

View

Download (PDF)

Tags: Computer science, Heterogeneous systems, nVidia, OmpSs, OpenCL, Tesla K20

January 13, 2019 by hgpu

Exact Selectivity Computation for Modern In-Memory Database Query Optimization

Jun Hyung Shin, Florin Rusu, Alex Suhan

View

Download (PDF)

Source codes

Tags: Computer science, CUDA, Databases, nVidia, Package, Tesla K80

January 13, 2019 by hgpu

BitCracker: BitLocker meets GPUs

Elena Agostini, Massimo Bernaschi

View

Download (PDF)

Source codes

Tags: AES, ATI, ATI Radeon HD 7990, Computer science, CUDA, nVidia, nVidia GeForce GTX Titan, nVidia GeForce GTX Titan X, OpenCL, Package, Security, Tesla K80, Tesla P100, Tesla V100

January 13, 2019 by hgpu

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Can Large Language Models Predict Parallel Code Performance?

PELSI: Power-Efficient Layer-Switched Inference

Efficient deep learning inference on end devices

Ouroboros: Virtualized Queues for dynamic memory management

Dynamic Memory Management on GPUs with SYCL

MSCCL++: A GPU-driven communication stack for scalable AI applications

MSCCL++: Rethinking GPU Communication Abstractions for Cutting-edge AI Applications

Benchmark compute shader of Unity against InteropUnityCUDA

InteropUnityCUDA: A Tool for Interoperability Between Unity and CUDA

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

Applications

Supporting mixed-datatype matrix multiplication within the BLIS framework

Efficient Implementation and Optimization of Geometric Multigrid Operations in the LIFT Framework

AccUDNN: A GPU Memory Efficient Accelerator for Training Ultra-deep Deep Neural Networks

Exploring FPGA-specific Optimizations for Irregular OpenCL Applications

AutoPhase: Compiler Phase-Ordering for High Level Synthesis with Deep Reinforcement Learning

Automatic acceleration of Numpy applications on GPUs and multicore CPUs

A Survey of FPGA Based Deep Learning Accelerators: Challenges and Opportunities

Tango: A Deep Neural Network Benchmark Suite for Various Accelerators

Vulkan 1.1.97 – A Specification (with all registered Vulkan extensions)

Auto-tuned OpenCL kernel co-execution in OmpSs for heterogeneous systems

Exact Selectivity Computation for Modern In-Memory Database Query Optimization

BitCracker: BitLocker meets GPUs

Recent source codes

SYCL Container

CASS: Cuda-Amd aSSembly

Cluser of smartphones for edge computing application using TensorFlow

CFAL-bench

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Can Large Language Models Predict Parallel Code Performance?

PELSI: Power-Efficient Layer-Switched Inference

Ouroboros: Virtualized Queues for dynamic memory management

MSCCL++: A GPU-driven communication stack for scalable AI applications

Benchmark compute shader of Unity against InteropUnityCUDA

Most viewed papers (last 30 days)