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tigr's Introduction

Tigr

Tigr is a lightweight graph transformation and processing framework for GPU platforms.

In real-world graphs, the high irregularity of degree distribution acts as a major barrier to their efficient processing on GPU architectures. Tigr addresses the irregularity issue at its origin by transforming irregular graphs into more regular ones, meanwhile preserving the same results as running on the original graphs.

Compilation

To compile Tigr, just run make in the root directory.

Running applications in Tigr

The applications take the input graph as input as well as some optional arguments. For example:

$ ./sssp --input path-to-input-graph
$ ./sssp --input path-to-input-graph --source 10

Input graph format

Input graphs should be in form of plain text files, containing the list of the edges of the graph. Each line is corresponding to an edge and is of the following form:

V1  V2  W

It specifies that there is an edge from node V1 to node V2 with weight W. The Wight value is optional and if it is omitted, it is set to 1. The node-ids can start from 0 or 1. It ignores any line starting with a character rather than a number.

Graphs in this format can be found in many public graph repositories, such as SNAP's. There are some graph datasets ready to download in datasets folder. To download, just run make in each folder.

Publications:

[ASPLOS'18] Amir Nodehi, Junqiao Qiu, Zhijia Zhao. Tigr: Transforming Irregular Graphs for GPU-Friendly Graph Processing. In Proceedings of The 23th International Conference on Architectural Support for Programming Languages and Operating Systems, Williamsburg, VA, 2018. 15 pages

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tigr's Issues

wrong CC for CUDA complication

Hi, I'm reading your paper. I notice that you are using P4000 GPU, which is with compute capability 6.1. However, the compile flag used in the code is SM32.

if you compile it with sm61, the performance should be even better.

will it be possible for you to do the experiments again to see the performance improvements?

Regards

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