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Divmap's Blog | Detecting and removing divergences from CUDA kernels

The growing interest in GPU programming has brought renewed attention to the Single Instruction Multiple Data (SIMD) execution model. SIMD machines give application developers a tremendous computational power; however, the model also bring restrictions. In particular, processing elements (PEs) execute in lock-step, and might lose performance due to divergences in the program's control flow. In…

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The growing interest in GPU programming has brought renewed attention to the Single Instruction Multiple Data (SIMD) execution model. SIMD machines give application developers a tremendous computational power; however, the model also bring restrictions. In particular, processing elements (PEs) execute in lock-step, and might lose performance due to divergences in the program's control flow. In…

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Divmap's Blog | Detecting and removing divergences from CUDA kernels | divmap.wordpress.com Reviews

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The growing interest in GPU programming has brought renewed attention to the Single Instruction Multiple Data (SIMD) execution model. SIMD machines give application developers a tremendous computational power; however, the model also bring restrictions. In particular, processing elements (PEs) execute in lock-step, and might lose performance due to divergences in the program's control flow. In…

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Related works | Divmap's Blog

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Detecting and removing divergences from CUDA kernels. There are a number of works that contains analysis similar to our divergence analysis. In particular, we cite Aiken and Gay’s barrier inference analysis. Stratton et al.’s variance analysis. And Scholz et al.’s input dependence analysis. The work that is the closes to ours is, no doubt, Aiken and Gay’s barrier inference analysis. In this work, the authors define the concept of. Aiken’s analysis relies on the traditional monotone framework. Barrier inf...

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April | 2010 | Divmap's Blog

https://divmap.wordpress.com/2010/04

Detecting and removing divergences from CUDA kernels. Archive for April, 2010. April 6, 2010. Max num div (num visits). Below we show also the optimization of the sum of prefixes. This application has been used by NVIDIA in many examples and tutorials. The code is heavily optimized, and we did not find many branches in the kernels:. Max num div (num visits). Max num div (num visits). For instance, take a look at Breath-First-Search:. Max num div (num visits). Max num div (num visits).

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Divergence Data | Divmap's Blog

https://divmap.wordpress.com/home/divergence-data

Detecting and removing divergences from CUDA kernels. The table below contains the data that we got while testing our divergence analysis implementation. We have tested it on 67 kernels, that we took from Nvidia’s SDK. We use the following captions:. Bench: the name of the benchmark suite: either rodinia, or Nvidia SDK. Prog: the name of the program in the benchmark suite. KernelName: the name of the kernel in the program. Time(ms): the time that Ocelot took to run our analysis on the kernel. Address nev...

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March | 2010 | Divmap's Blog

https://divmap.wordpress.com/2010/03

Detecting and removing divergences from CUDA kernels. Archive for March, 2010. How to Instrument PTX code. March 28, 2010. In order to perform dynamic measurements on GPU code, a number of steps must be performed. It is necessary to change the CUDA program, and the PTX kernel. The former we do manually, the latter we do through our version of the Ocelot compiler. Instrumenting a simple kernel using Ocelot. Fills a vector with random float entries. Print the sum of the vector. Dot prod = %lf n. VEC SIZE ,.

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What are divergences? | Divmap's Blog

https://divmap.wordpress.com/home/div_def

Detecting and removing divergences from CUDA kernels. Is divergent if there exist two PEs. At the same moment. For instance, let’s consider the program below:. This is a very simple, and rather artificial assembly, that implements the following algorithm:. If (SM[tid] 0) { SM[tid] = 10 } else { SM[tid] -= 10 }. The set of mSIMD instructions that implement this program start by loading into. The value stored at. The value that will be stored at. Right after the instruction. Divergent variables might cause.

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Divmap's Blog | Detecting and removing divergences from CUDA kernels

Detecting and removing divergences from CUDA kernels. Divergence Analysis and Optimizations. To identify chains of similarities between two possibly divergent program paths, and weave these paths together as much as possible. We have implemented. Our techniques on the Ocelot open-source CUDA compiler. And have tested it on many industrial-strength GPU benchmarks, including Rodinia. And the Nvidia’s SDK. Our implementation is freely available. For a more concise tutorial, see the slides.

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