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BrainFrame: A heterogeneous accelerator platform for neuron simulations

Georgios Smaragdos, Georgios Chatzikonstantis, Rahul Kukreja, Harrys Sidiropoulos, Dimitrios Rodopoulos, Ioannis Sourdis, Zaid Al-Ars, Christoforos Kachris, Dimitrios Soudris, Chris I. De Zeeuw, Christos Strydis

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Tags: Biophysics, Computer science, Heterogeneous systems, Intel Xeon Phi, Neural and Evolutionary Computing, nVidia, nVidia GeForce GTX Titan X, OpenMP

December 10, 2016 by hgpu

ESE: Efficient Speech Recognition Engine with Compressed LSTM on FPGA

Song Han, Junlong Kang, Huizi Mao, Yiming Hu, Xin Li, Yubin Li, Dongliang Xie, Hong Luo, Song Yao, Yu Wang, Huazhong Yang, William J. Dally

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Tags: Computer science, CUDA, Deep learning, FPGA, LSTM, Machine learning, nVidia, nVidia GeForce GTX Titan X, Speech recognition

December 6, 2016 by hgpu

A Comparison of GPU Execution Time Prediction using Machine Learning and Analytical Modeling

Marcos Amaris, Raphael Y. de Camargo, Mohamed Dyab, Alfredo Goldman, Denis Trystram

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Tags: Analytical model, Benchmarking, Computer science, Heterogeneous systems, Machine learning, nVidia, nVidia GeForce GTX 680, nVidia GeForce GTX 970, nVidia GeForce GTX 980, nVidia GeForce GTX Titan, nVidia GeForce GTX Titan Black, nVidia GeForce GTX Titan X, nVidia Quadro K5200, Package, Performance, Tesla K20, Tesla K40

December 6, 2016 by hgpu

OpenACC cache Directive: Opportunities and Optimizations

Ahmad Lashgar, Amirali Baniasadi

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Tags: Code generation, Computer science, CUDA, nVidia, OpenACC, OpenCL, Package, Performance, Tesla K20

December 3, 2016 by hgpu

Accelerating string tokenization with FPGAs for IoT data handling equipment

Kazuhiro Yamato

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Tags: Computer science, FPGA, IoT, OpenCL

December 3, 2016 by hgpu

Should I use TensorFlow?

Martin Schrimpf

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Tags: Computer science, CUDA, Deep learning, Machine learning, nVidia, Performance, Python, TensorFlow

December 3, 2016 by hgpu

Hardware thread reordering to boost OpenCL throughput on FPGAs

Amir Momeni, Hamed Tabkhi, Gunar Schirner, David Kaeli

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Tags: Computer science, FPGA, Hardware, OpenCL, Performance

November 30, 2016 by hgpu

Parallelizing Word2Vec in Multi-Core and Many-Core Architectures

Shihao Ji, Nadathur Satish, Sheng Li, Pradeep Dubey

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Tags: Computer science, CUDA, Deep learning, Intel Xeon Phi, NLP, nVidia, nVidia GeForce GTX Titan X, Performance, Tesla K40

November 30, 2016 by hgpu

Optimization of Pattern Matching Algorithms for Multi- and Many-Core Platforms

Pedro Miguel Marques Pereira

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Tags: Algorithms, AMD Radeon R9 290X, ARM, ATI, ATI Radeon HD 7970, Compression, Computer science, nVidia, nVidia GeForce GTX 480, nVidia GeForce GTX 570 M, nVidia GeForce GTX 680, nVidia GeForce GTX 750 Ti, nVidia GeForce GTX Titan Black, nVidia Jetson TK1, OpenCL, OpenMP, Thesis

November 30, 2016 by hgpu

Deep, Dense, and Low-Rank Gaussian Conditional Random Fields

Siddhartha Chandra, Iasonas Kokkinos

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Tags: Caffe, Computer science, Computer vision, CUDA, nVidia, nVidia GeForce GTX 1080

November 30, 2016 by hgpu

A Metric for Performance Portability

S. J. Pennycook, J. D. Sewall, V. W. Lee

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Tags: Computer science, CUDA, OpenACC, OpenCL, Performance, performance portability

November 25, 2016 by hgpu

Fast and Energy-Efficient CNN Inference on IoT Devices

Mohammad Motamedi, Daniel Fong, Soheil Ghiasi

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Tags: CNN, Computer science, Deep learning, Java, Machine learning, Neural networks, Package, RenderScript, SoC

November 25, 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

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high performance computing on graphics processing units: hgpu.org

BrainFrame: A heterogeneous accelerator platform for neuron simulations

ESE: Efficient Speech Recognition Engine with Compressed LSTM on FPGA

A Comparison of GPU Execution Time Prediction using Machine Learning and Analytical Modeling

OpenACC cache Directive: Opportunities and Optimizations

Accelerating string tokenization with FPGAs for IoT data handling equipment

Should I use TensorFlow?

Hardware thread reordering to boost OpenCL throughput on FPGAs

Parallelizing Word2Vec in Multi-Core and Many-Core Architectures

Optimization of Pattern Matching Algorithms for Multi- and Many-Core Platforms

Deep, Dense, and Low-Rank Gaussian Conditional Random Fields

A Metric for Performance Portability

Fast and Energy-Efficient CNN Inference on IoT Devices

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

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