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MobiRNN: Efficient Recurrent Neural Network Execution on Mobile GPU

Qingqing Cao, Niranjan Balasubramanian, Aruna Balasubramanian

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Tags: Computer science, CUDA, Deep learning, Java, Neural networks, nVidia, Package, RNN, TensorFlow

June 10, 2017 by hgpu

Neneta: Heterogeneous Computing Complex-Valued Neural Network Framework

Vladimir Lekic, Zdenka Babic

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Tags: ATI, ATI Radeon HD 5770, Computer science, Deep learning, Heterogeneous systems, Neural networks, OpenCL

June 5, 2017 by hgpu

Optimizing Memory Efficiency for Convolution Kernels on Kepler GPUs

Xiaoming Chen, Jianxu Chen, Danny Z. Chen, Xiaobo Sharon Hu

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Tags: CNN, Computer science, Computer vision, CUDA, Deep learning, Neural networks, nVidia, Tesla K40

June 1, 2017 by hgpu

Implementing Efficient, Portable Computations for Machine Learning

Matthew Walter Moskewicz

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Tags: Algorithms, AMD R9 Nano, ATI, Computer science, Computer vision, CUDA, Deep learning, Machine learning, Neural networks, nVidia, nVidia GeForce GTX Titan X, OpenCL, Package, Thesis

May 24, 2017 by hgpu

Espresso: Efficient Forward Propagation for BCNNs

Fabrizio Pedersoli, George Tzanetakis, Andrea Tagliasacchi

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Tags: Computer science, CUDA, Deep learning, Neural networks, nVidia, nVidia GeForce GTX 960

May 24, 2017 by hgpu

Efficient Parallel Methods for Deep Reinforcement Learning

Alfredo V. Clemente, Humberto N. Castejon, Arjun Chandra

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Tags: Algorithms, Computer science, CUDA, Deep learning, nVidia, nVidia GeForce GXT 980 Ti, Package, Python, TensorFlow

May 18, 2017 by hgpu

A Design Methodology for Efficient Implementation of Deconvolutional Neural Networks on an FPGA

Xinyu Zhang, Srinjoy Das, Ojash Neopane, Ken Kreutz-Delgado

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Tags: Algorithms, Computer science, Computer vision, Deep learning, DSP, FPGA, Neural networks

May 11, 2017 by hgpu

DeepMetabolism: A Deep Learning System to Predict Phenotype from Genome Sequencing

Weihua Guo, You Xu, Xueyang Feng

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Tags: Biology, Computer science, Deep learning, Medicine, nVidia, nVidia GeForce GTX 1080, Package, Python, TensorFlow

May 11, 2017 by hgpu

Acceleration of Deep Learning on FPGA

Huyuan Li

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Tags: CNN, Computer science, Deep learning, FPGA, Neural networks, nVidia, OpenCL, Tesla K40, Thesis

May 9, 2017 by hgpu

Compressing DMA Engine: Leveraging Activation Sparsity for Training Deep Neural Networks

Minsoo Rhu, Mike O'Connor, Niladrish Chatterjee, Jeff Pool, Stephen W. Keckler

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

May 6, 2017 by hgpu

TinyDL: Just-In-Time Deep Learning Solution For Constrained Embedded Systems

Bita Darvish Rouhani, Azalia Mirhoseini, Farinaz Koushanfar

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Tags: ARM, Computer science, CUDA, Deep learning, nVidia, nVidia Jetson TK1, Performance

May 2, 2017 by hgpu

Deep Learning in the Automotive Industry: Applications and Tools

Andre Luckow, Matthew Cook, Nathan Ashcraft, Edwin Weill, Emil Djerekarov, Bennie Vorster

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Tags: Cloud, Computer science, Computer vision, CUDA, Deep learning, Machine learning, Neural networks, nVidia, nVidia GeForce GTX Titan X, nVidia GRID K520, Performance

May 2, 2017 by hgpu

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

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

Optical flow algorithms for SYCL

SYCL in the edge: performance and energy evaluation for heterogeneous acceleration

OpenMP5-Offload-OpenMC-Intel-PVC

Distributed OpenMP Offloading of OpenMC on Intel GPU MAX Accelerators

See all packages

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high performance computing on graphics processing units: hgpu.org

MobiRNN: Efficient Recurrent Neural Network Execution on Mobile GPU

Neneta: Heterogeneous Computing Complex-Valued Neural Network Framework

Optimizing Memory Efficiency for Convolution Kernels on Kepler GPUs

Implementing Efficient, Portable Computations for Machine Learning

Espresso: Efficient Forward Propagation for BCNNs

Efficient Parallel Methods for Deep Reinforcement Learning

A Design Methodology for Efficient Implementation of Deconvolutional Neural Networks on an FPGA

DeepMetabolism: A Deep Learning System to Predict Phenotype from Genome Sequencing

Acceleration of Deep Learning on FPGA

Compressing DMA Engine: Leveraging Activation Sparsity for Training Deep Neural Networks

TinyDL: Just-In-Time Deep Learning Solution For Constrained Embedded Systems

Recent source codes

QArray

Celerity: High-level C++ for Accelerator Clusters

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

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

Optical flow algorithms for SYCL

OpenMP5-Offload-OpenMC-Intel-PVC

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