high performance computing on graphics processing units: hgpu.org

hgpu.org » Computer science

PVR: Patch-to-Volume Reconstruction for Large Area Motion Correction of Fetal MRI

Amir Alansary, Bernhard Kainz, Martin Rajchl, Maria Murgasova, Mellisa Damodaram, David F.A. Lloyd, Alice Davidson, Steven G. McDonagh, Mary Rutherford, Joseph V. Hajnal, Daniel Rueckert

View

Download (PDF)

Source codes

Tags: Computer science, CUDA, Magnetic resonance imaging, Motion compensation, MRI, nVidia, Package, Tesla K40

November 25, 2016 by hgpu

Efficient Kernel Synthesis for Performance Portable Programming

Li-Wen Chang, Izzat El Hajj, Christopher Rodrigues, Juan Gomez-Luna, Wen-mei Hwu

View

Download (PDF)

Tags: Code generation, Computer science, CUDA, Design space exploration, Heterogeneous systems, nVidia, performance portability, Tesla C2050, Tesla K20

November 25, 2016 by hgpu

dMath: Distributed Linear Algebra for DL

Steven Eliuk, Cameron Upright, Hars Vardhan, Stephen Walsh, Trevor Gale

View

Download (PDF)

Tags: Algorithms, Computer science, CUDA, Deep learning, Linear Algebra, Matrix multiplication, MPI, Neural networks, nVidia, Tesla K80

November 25, 2016 by hgpu

A Metaprogramming and Autotuning Framework for Deploying Deep Learning Applications

Matthew W. Moskewicz, Ali Jannesari, Kurt Keutzer

View

Download (PDF)

Source codes

Tags: AMD R9 Nano, ARM, ATI, Code generation, Computer science, Computer vision, CUDA, Deep learning, Neural networks, nVidia, nVidia GeForce GTX Titan X, OpenCL, Package

November 23, 2016 by hgpu

Performance Analysis of CUDA and OpenCL By Implementation of Cryptographic Algorithms

Manas Mahapatra

View

Download (PDF)

Tags: Computer science, CUDA, nVidia, nVidia GeForce GT 525 M, OpenCL, Performance, Security, Thesis

November 23, 2016 by hgpu

Deep Tensor Convolution on Multicores

David Budden, Alexander Matveev, Shibani Santurkar, Shraman Ray Chaudhuri, Nir Shavit

View

Download (PDF)

Tags: Computer science, CUDA, Deep learning, Neural networks, nVidia, nVidia GeForce GTX Titan X

November 23, 2016 by hgpu

GA3C: GPU-based A3C for Deep Reinforcement Learning

Mohammad Babaeizadeh, Iuri Frosio, Stephen Tyree, Jason Clemons, Jan Kautz

View

Download (PDF)

Tags: Computer science, CUDA, Deep learning, Neural networks, nVidia, nVidia GeForce GTX Titan X, TensorFlow

November 23, 2016 by hgpu

Processing OLTP Workloads on Hybrid CPU/GPU Systems

Mudit Bhatnagar

View

Download (PDF)

Tags: Computer science, Databases, FPGA, Heterogeneous systems, nVidia, nVidia GeForce GT 640, nVidia GeForce GTX Titan, OpenCL, Thesis

November 22, 2016 by hgpu

SC-DCNN: Highly-Scalable Deep Convolutional Neural Network using Stochastic Computing

Ao Ren, Ji Li, Zhe Li, Caiwen Ding, Xuehai Qian, Qinru Qiu, Bo Yuan, Yanzhi Wang

View

Download (PDF)

Tags: ASIC, CNN, Computer science, Deep learning, FPGA, Neural networks, nVidia, Tesla C2075

November 22, 2016 by hgpu

Evaluation of an OpenCL-Based FPGA Platform for Particle Filter

Shunsuke Tatsumi, Masanori Hariyama, Norikazu Ikoma

View

Download (PDF)

Tags: Computer science, FPGA, OpenCL, Particle filtering

November 19, 2016 by hgpu

HIPAcc: A Domain-Specific Language and Compiler for Image Processing

Richard Membarth, Oliver Reiche, Frank Hannig, Jurgen Teich, Mario Korner, Wieland Eckert

View

Download (PDF)

Source codes

Tags: Algorithms, AMD Radeon R9 290X, ARM, ATI, Compilers, Computer science, CUDA, Heterogeneous systems, Image processing, Intel Xeon Phi, nVidia, OpenCL, Package, Tesla K20

November 19, 2016 by hgpu

How to scale distributed deep learning?

Peter H. Jin, Qiaochu Yuan, Forrest Iandola, Kurt Keutzer

View

Download (PDF)

Tags: Computer science, CUDA, Deep learning, GPU cluster, nVidia, Performance, Tesla K20, Tesla K80

November 19, 2016 by hgpu

HipKittens: Fast and Furious AMD Kernels

CudaForge: An Agent Framework with Hardware Feedback for CUDA Kernel Optimization

LC Framework

Characterizing the Performance of Parallel Data-Compression Algorithms across Compilers and GPUs

pplx-garden: Perplexity open source garden for inference technology

RDMA Point-to-Point Communication for LLM Systems

OpScanner

A Study of Floating-Point Precision Tuning in Deep Learning Operators Implementations

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

Scalable GPU-Based Integrity Verification for Large Machine Learning Models

transformers_tvm: Implementation of Encoder Decoder transformer on TVM

Enhancing Transformer Performance and Portability through Auto-tuning Frameworks

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

PVR: Patch-to-Volume Reconstruction for Large Area Motion Correction of Fetal MRI

Efficient Kernel Synthesis for Performance Portable Programming

dMath: Distributed Linear Algebra for DL

A Metaprogramming and Autotuning Framework for Deploying Deep Learning Applications

Performance Analysis of CUDA and OpenCL By Implementation of Cryptographic Algorithms

Deep Tensor Convolution on Multicores

GA3C: GPU-based A3C for Deep Reinforcement Learning

Processing OLTP Workloads on Hybrid CPU/GPU Systems

SC-DCNN: Highly-Scalable Deep Convolutional Neural Network using Stochastic Computing

Evaluation of an OpenCL-Based FPGA Platform for Particle Filter

HIPAcc: A Domain-Specific Language and Compiler for Image Processing

How to scale distributed deep learning?

Recent source codes

HipKittens: Fast and Furious AMD Kernels

Fortran xDSL dialects

mt4g: Memory Topology 4 GPUs

Falcon: GPU-Based Floating-point Adaptive Lossless Compression

CudaForge: An Agent Framework with Hardware Feedback for CUDA Kernel Optimization

LC Framework

pplx-garden: Perplexity open source garden for inference technology

OpScanner

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

transformers_tvm: Implementation of Encoder Decoder transformer on TVM

Most viewed papers (last 30 days)