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Zorua: Enhancing Programming Ease, Portability, and Performance in GPUs by Decoupling Programming Models from Resource Management

Nandita Vijaykumar, Kevin Hsieh, Gennady Pekhimenko, Samira Khan, Ashish Shrestha, Saugata Ghose, Phillip B. Gibbons, Onur Mutlu

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Tags: Computer science, CUDA, Hardware Architecture, nVidia, Virtualization

February 10, 2018 by hgpu

Lost in Abstraction: Pitfalls of Analyzing GPUs at the Intermediate Language Level

Anthony Gutierrez, Bradford M. Beckmann, Alexandru Dutu, Joseph Gross, John Kalamatianos, Onur Kayiran, Michael LeBeane, Matthew Poremba, Brandon Potter, Sooraj Puthoor, Matthew D. Sinclair, Mark Wyse, Jieming Yin, Xianwei Zhang, Akshay Jain, Timothy G. Rogers

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Tags: ATI IL, Computer science, HSA

February 10, 2018 by hgpu

Accelerating Deep Neural Networks on Low Power Heterogeneous Architectures

Manolis Loukadakis, Jose Cano, Michael O'Boyle

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Tags: ARM, Computer science, Deep learning, Heterogeneous systems, Neural networks, OpenCL

February 10, 2018 by hgpu

Ikra-Cpp: A C++/CUDA DSL for Object-Oriented Programming with Structure-of-Arrays Layout

Matthias Springer, Hidehiko Masuhara

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Tags: Computer science, CUDA, Data Structures and Algorithms, DSL, nVidia, Package

February 9, 2018 by hgpu

Interactive GPU active contours for segmenting inhomogeneous objects

Chris G. Willcocks, Philip T. G. Jackson, Carl J. Nelson, Amar V. Nasrulloh, Boguslaw Obara

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Tags: Biology, Image processing, Medicine, nVidia, nVidia GeForce GTX Titan X, OpenCL, Package

February 9, 2018 by hgpu

Combined Spatial and Temporal Blocking for High-Performance Stencil Computation on FPGAs Using OpenCL

Hamid Reza Zohouri, Artur Podobas, Satoshi Matsuoka

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Tags: Computer science, FPGA, OpenCL, Stencil computation

February 9, 2018 by hgpu

MiMatrix: A Massively Distributed Deep Learning Framework on a Petascale High-density Heterogeneous Cluster

Xin Chen, Hua Zhou, Yuxiang Gao, Yu Zhu, Dongyan Wang

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Tags: Algorithms, Computer science, CUDA, Deep learning, GPU cluster, Heterogeneous systems, nVidia, RDMA, Tesla P100

February 9, 2018 by hgpu

Tuning Streamed Applications on Intel Xeon Phi: A Machine Learning Based Approach

Peng Zhang, Jianbin Fang, Tao Tang, Canqun Yang, Zheng Wang

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Tags: Benchmarking, Computer science, Intel Xeon Phi, Machine learning, Package, Performance

February 9, 2018 by hgpu

REOH: Runtime Energy Optimization for Heterogeneous Systems

Vi Ngoc-Nha Tran, Tommy Oines, Alexander Horsch, Phuong Hoai Ha

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Tags: Computer science, Heterogeneous systems, nVidia, nVidia Quadro K620, OpenCL, Package

February 3, 2018 by hgpu

Accelerating recurrent neural network language model based online speech recognition system

Kyungmin Lee, Chiyoun Park, Namhoon Kim, Jaewon Lee

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Tags: Computer science, CUDA, Deep learning, Neural networks, nVidia, RNN, Speech recognition, Tesla K80

February 3, 2018 by hgpu

A Collective Knowledge workflow for collaborative research into multi-objective autotuning and machine learning techniques

Grigori Fursin, Anton Lokhmotov, Dmitry Savenko, Eben Upton

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Tags: ARM, Computer science, Machine learning, OpenCL

February 3, 2018 by hgpu

Efficient SIMD Vectorization for Hashing in OpenCL

Tobias Behrens, Viktor Rosenfeld, Jonas Traub, Sebastian Bress, Volker Markl

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Tags: Computer science, Databases, Hashing, Intel Xeon Phi, OpenCL

February 3, 2018 by hgpu

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

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

INT v.s. FP: A Comprehensive Study of Fine-Grained Low-bit Quantization Formats

AutoDock-GPU: AutoDock for GPUs and other accelerators

Architecting Tensor Core-Based Reductions for Irregular Molecular Docking Kernels

NCCLX: collective communication framework

Collective Communication for 100k+ GPUs

Tutoring LLM into a Better CUDA Optimizer

Tutoring LLM into a Better CUDA Optimizer

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

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

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

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