A coordinated tiling and batching framework for efficient

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A coordinated tiling and batching framework for efficient ...

It is a two-phase framework, which consists of a tiling engine and a batching engine to perform efficient batched GEMM on GPUs. Tiling engine partitions the

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A Coordinated Tiling and Batching Framework for Efficient ...

A Coordinated Tiling and Batching Framework for Efficient GEMM on GPUs Xiuhong Li1, Yun Liang1,∗, Shengen Yan2, Liancheng Jia1, Yinghan Li2 1 Center for

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A Coordinated Tiling and Batching Framework for Efficient ...

Feb 19, 2019  In this paper, we propose a coordinated tiling and batching framework for accelerating GEMM on GPUs. It is a two-phase framework, which consists of a tiling engine

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A coordinated tiling and batching framework for efficient ...

Request PDF A coordinated tiling and batching framework for efficient GEMM on GPUs General matrix multiplication (GEMM) plays a paramount role in a broad range of

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"A coordinated tiling and batching framework for efficient ...

Bibliographic details on A coordinated tiling and batching framework for efficient GEMM on GPUs. We would like to express our heartfelt thanks to the many users who

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PPoPP '19- Proceedings of the 24th Symposium on Principles ...

In this paper, we propose a coordinated tiling and batching framework for accelerating GEMMs on GPUs. It is a two-phase framework, which consists of a tiling

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Eric Liang - PPoPP 2019

A Coordinated Tiling and Batching Framework for Efficient GEMM on GPUs: Show activities from other conferences: Share. PPoPP 2019-profile View general profile. PPoPP

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Publications - Yun (Eric) Liang’s Homepage

“ A Coordinated Tiling and Batching Framework for Efficient GEMM on GPUs,” to appear in the proceedings of Principles and Practice of Parallel Programming (PPoPP), February 2019. Best Paper Award Nomination.

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Performance optimization of convolution calculation by ...

Sep 22, 2019  ”A coordinated tiling and batching framework for efficient GEMM on GPUs.” Proceedings of the 24th Symposium on Principles and Practice of Parallel Programming. ACM, 2019. [13] Zhang, Chen, et al. ”Caffeine: Towards uniformed representation and acceleration for deep convolutional neural networks.” IEEE Transactions on Computer-Aided ...

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‪Xiuhong Li(李秀红)‬ - ‪Google Scholar‬

A coordinated tiling and batching framework for efficient GEMM on GPUs X Li, Y Liang, S Yan, L Jia, Y Li Proceedings of the 24th Symposium on Principles and Practice of Parallel , 2019

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Memory-Optimized Wavefront Parallelism on GPUs ...

Mar 25, 2020  A coordinated tiling and batching framework for efficient GEMM on GPUS. In: Proceedings of the 24th Symposium on Principles and Practice of

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GitHub - LiXiuhong/batched_gemm

After tiling engine, it generates multiple tiles from the GEMMs. In the batching engine, it is responsible to assign the tiles into thread blocks. We design a series of batching algorithms to determine the assignment from tiles to thread blocks. Then, we propose a general programming style to describe the coordinated tiling and batching solution.

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Reproducible papers with code and validated results

18) A Coordinated Tiling and Batching Framework for Efficient GEMM on GPUs Xiuhong Li, Yun Liang, Shengen Yan, Liancheng Jia, Yinghan Li. 10.1145/3300174: 19) Lightweight Hardware Transactional Memory Profiling Qingsen Wang, Pengfei Su, Milind Chabbi, Xu Liu. 10.1145/3300175: 20) Adaptive Sparse Tiling for Sparse Matrix Multiplication

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clearlyhunch

'A Coordinated Tiling and Batching Framework for Efficient GEMM on GPUs' 라는 논문이 습득하고자 하는 정보를 담은 논문이라고 판단하였다. 논문에서 한번의 타일링만 하는 것이 아니라 여러가지 구조로 타일링을 할 수 있다는 것을 볼 수 있었다.

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Coordinated static and dynamic cache bypassing for GPUs ...

It is a two-phase framework, which consists of a tiling engine and a batching engine to perform efficient batched GEMM on GPUs. Tiling engine partitions the GEMMs into independent tiles and ...

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高能效计算与应用中心梁云研究员课题组两篇论文被计算机体系结

日前,高能效计算与应用中心梁云研究员课题组在GPU-FPGA(图形处理器-现场可编程门阵列)异构系统运行时管理和GPU上高性能矩阵乘法方面的工作双双取得获得重要突破,相关成果以学术论文《面向交互式应用的高效异构系统和应用管理技术》 (Poly: efficient heterogeneous system and application management for ...

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Collective Knowledge platform

Collective Knowledge platform. [ Project overview, Reddit disccusion , Android app , Chrome add-on ] lib Performance Using Direct Virtual Hardware. lib 0sim: Preparing System Software for a World with Terabyte-scale Memories. lib A Compiler Infrastructure for Accelerator Generators. lib A Coordinated Tiling and Batching Framework for Efficient ...

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李秀红 - Researcher - SenseTime 商汤科技 LinkedIn

A Coordinated Tiling and Batching Framework for Efficient GEMM on GPUs ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (PPoPP) 2018 年 12 月 16 日 一种新型矩阵分块决策算法和批执行决策算法,显著提高小矩阵算法的计算效率,从而克服现实应用中诸多小矩阵算

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[PDF] Accelerating Sparse Approximate Matrix ...

Although the matrix multiplication plays a vital role in computational linear algebra, there are few efficient solutions for matrix multiplication of the near-sparse matrices. The Sparse Approximate Matrix Multiply (SpAMM) is one of the algorithms to fill the performance gap neglected by traditional optimizations for dense/sparse matrix multiplication.

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Reproducible papers with code and validated results

18) A Coordinated Tiling and Batching Framework for Efficient GEMM on GPUs Xiuhong Li, Yun Liang, Shengen Yan, Liancheng Jia, Yinghan Li. 10.1145/3300174: 19) Lightweight Hardware Transactional Memory Profiling Qingsen Wang, Pengfei Su, Milind Chabbi, Xu Liu. 10.1145/3300175: 20) Adaptive Sparse Tiling for Sparse Matrix Multiplication

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Tile and list caching for workspaces - Finance ...

Jun 20, 2017  Tile and list caching for workspaces. 06/20/2017; 10 minutes to read; j; R; In this article. It's important that workspaces perform well, and that they be responsive (that is, the data that appears in a workspace is refreshed as expected and kept up to date).

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Memory-Optimized Wavefront Parallelism on GPUs ...

Mar 25, 2020  A coordinated tiling and batching framework for efficient GEMM on GPUS. In: Proceedings of the 24th Symposium on Principles and Practice of

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clearlyhunch

'A Coordinated Tiling and Batching Framework for Efficient GEMM on GPUs' 라는 논문이 습득하고자 하는 정보를 담은 논문이라고 판단하였다. 논문에서 한번의 타일링만 하는 것이 아니라 여러가지 구조로 타일링을 할 수 있다는 것을 볼 수 있었다.

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Cost-efficient coordinated scheduling for leasing cloud ...

May 01, 2015  Coordinated scheduling reshapes the batch jobs, considers the remaining capacity, and uses spot instances, if necessary, to complete the batch jobs in a cost-efficient way. If the remaining capacity is not sufficient for a batch workload, the residual batch jobs are scheduled to run when the spot instance price is lower within the deadline.

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A framework for building energy management system with ...

Jan 05, 2021  Efficient utilization of a residential photovoltaic (PV) array with grid connection is difficult due to power fluctuation and geographical dispersion. Reliable energy management and control system are required for overcoming these obstacles. This study provides a new residential energy management system (REMS) based on the convolution neural network (CNN) including PV array environment. The ...

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Python Frameworks Top 20 Different Framewroks of Python ...

1. Django. One of the most widely used python frameworks is a high-level framework that encourages clean and efficient design. the various development works possible with Django are, 1. Creating and deploying RESTapi. 2. web application deployment. 3. Performance improvement through web application caching. 4.

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Deep-dive into Convolutional Networks by Antonino ...

Mar 20, 2019  Figure 1.1 Convolution of a 5x5 input (blue) with 3x3 kernel (grey) with a stride of 2 and padding of 1. The 3x3 output is in green ().Both classical and deep-learning convolution compute the output by applying kernel to an input array. Each output pixel is the sum of the element-by-element product between input and kernel (dot product).By shifting the kernel over the input, we obtain the ...

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WCF/MSMQ and transacted batching - get messages in batch ...

Jul 05, 2012  With transacted batching I am indeed given N (100, 1000, etc) messages per transaction, but they all come in my WCF client sequentially. So the service operation is called N times, once for each message. The transaction is committed or rolled back by the framework after the operation returns for the last message .

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The Complete Guide to Time Blocking - Todoist

Task batching. Task batching is when you group similar (usually smaller) tasks together and schedule specific time blocks to complete all at once. By tackling similar tasks in a group, you’ll limit the amount of context switching you have to do throughout your day, saving precious time and mental energy.

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End-to-End Optimization of Deep Learning Applications

layer and selected the best tiling factor accordingly. Experimental results show that dynamic tiling can speedup the performance of the whole network by 1.7×. Challenge 2: Integration overheads of using FPGA in ML frameworks: When processing a CNN application in modern ML framework such as TensorFlow [1], the complete stack consists

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[PDF] Accelerating Sparse Approximate Matrix ...

Although the matrix multiplication plays a vital role in computational linear algebra, there are few efficient solutions for matrix multiplication of the near-sparse matrices. The Sparse Approximate Matrix Multiply (SpAMM) is one of the algorithms to fill the performance gap neglected by traditional optimizations for dense/sparse matrix multiplication.

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(PDF) A Survey of Techniques for Optimizing Deep Learning ...

The rise of deep-learning (DL) has been fuelled by the improvements in accelerators. Due to its unique features, the GPU continues to remain the most widely used accelerator for DL applications. In this paper, we present a survey of architecture and

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(PDF) Matrix computations on the GPU. CUBLAS, CUSOLVER and ...

Matrix computations on the GPU. CUBLAS, CUSOLVER and MAGMA by example. Version 2017

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