Category Archives: ParaGrapher

Selective Parallel Loading of Large-Scale Compressed Graphs with ParaGrapher – arXiv Version

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PDF version
DOI: 10.48550/arXiv.2404.19735

Comprehensive evaluation is one of the basis of experimental science. In High-Performance Graph Processing, a thorough evaluation of contributions becomes more achievable by supporting common input formats over different frameworks. However, each framework creates its specific format, which may not support reading large-scale real-world graph datasets. This shows a demand for high-performance libraries capable of loading graphs to (i) accelerate designing new graph algorithms, (ii) to evaluate the contributions on a wide range of graph algorithms, and (iii) to facilitate easy and fast comparison over different graph frameworks.

To that end, we present ParaGrapher, a high-performance API and library for loading large-scale and compressed graphs. ParaGrapher supports different types of requests for accessing graphs in shared- and distributed-memory and out-of-core graph processing. We explain the design of ParaGrapher and present a performance model of graph decompression, which is used for evaluation of ParaGrapher over three storage types.

Our evaluation shows that by decompressing compressed graphs in WebGraph format, ParaGrapher delivers up to 3.2 times speedup in loading and up to 5.2 times speedup in end-to-end execution in comparison to the binary and textual formats.

ParaGrapher is available online on https://blogs.qub.ac.uk/DIPSA/ParaGrapher/.

BibTex

@misc{paragrapher-arxiv,
  title = { Selective Parallel Loading of Large-Scale 
            Compressed Graphs with ParaGrapher}, 
  author = { {Mohsen} {Koohi Esfahani} and Marco D'Antonio and 
             Syed Ibtisam Tauhidi and Thai Son Mai and 
             Hans Vandierendonck},
  year = {2024},
  eprint = {2404.19735},
  archivePrefix = {arXiv},
  primaryClass = {cs.AR},
  doi = {10.48550/arXiv.2404.19735}
}

Related Posts & Source Code

ParaGrapher Web Page

An Evaluation of Bandwidth of Different Storage Types (HDD vs. SSD vs. LustreFS) for Different Block Sizes and Different Parallel Read Methods (mmap vs pread vs read)

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Short URL of this post: https://blogs.qub.ac.uk/DIPSA/HDD-vs-SSD-vs-LustreFS-2024

We evaluate read bandwidth of three storage types:

  • HDD: A 6TB Hitachi HUS726060AL 7200RPM SATA v3.1
  • SSD: A 4TB Samsung MZQL23T8HCLS-00A07 PCIe4 NVMe v1.4
  • LustreFS: A parallel file system with total 2PB with a SSD pool

and for three parallel read methods:

and for two block sizes:

  • 4 KB blocks
  • 4 MB blocks

The source code is available on ParaGrapher repository:

The OS cache of storage contents have been dropped after each evaluation
(sudo sh -c 'echo 3 >/proc/sys/vm/drop_caches').
The flushcache.c file (https://github.com/DIPSA-QUB/ParaGrapher/blob/main/test/flushcache.c) can be used with the same functionality for users without sudo access, however, it usually takes more time to be finished.

For LustreFS, we have repeated the evaluation of read and pread using O_DIRECT flag as this flag prevents client-side caching.

For HDD and SSD experiments, we have used a machine with Intel W-2295 3.00GHz CPU, 18 cores, 36 hyper-threads, 24MB L3 cache, 256 GB DDR4 2933Mhz memory, running Debian 12 Linux 6.1. For LustreFS, we have used a machine with 2TB 3.2GHz DDR4 memory, 2 AMD 7702 CPUs, in total, 128 cores, 256 threads.

The results of the evaluation using read_bandwidth.c are in the following table. The values are Bandwidth in MB/s. Also, 1-2 digits close to each number with a white background are are percentage of load imbalance between parallel threads.

Please click on the image to expand.

C vs. Java

We measure the bandwidth of SSD and HDD in C (mmap and pread) vs. Java (mmap and read). We use a machine with Intel W-2295 3.00GHz CPU, 18 cores, 36 hyper-threads, 24MB L3 cache, 256 GB DDR4 2933Mhz memory, running Debian 12 Linux 6.1 and the following codes:

The results are in the following.


For similar comparisons you may refer to:
– https://github.com/david-slatinek/c-read-vs.-mmap/tree/main
– https://eklausmeier.goip.de/blog/2016/02-03-performance-comparison-mmap-versus-read-versus-fread/

Technical Posts


ParaGrapher

ParaGrapher Integrated to LaganLighter

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Poplar source code has been integrated to LaganLighter and access to different WebGraph formats are available in LaganLighter:

  • PARAGRAPHER_CSX_WG_400_AP
  • PARAGRAPHER_CSX_WG_404_AP
  • PARAGRAPHER_CSX_WG_800_AP

For further details, please refer to
– LaganLighter source coder Repository: https://github.com/DIPSA-QUB/LaganLighter, particularly, the graph.c file.
– ParaGrapher source code repository: https://github.com/DIPSA-QUB/ParaGrapher particularly, the src/webgraph.c and src/WG*.java files.

Read more about ParaGrapher and LaganLighter.

Related Posts

ParaGrapher Source Code For WebGraph Types

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ParaGrapher source code for accessing WebGraphs have been published. The supported graph types are:

ParaGrapher uses its asynchronous and parallel API to implement these graph types. The user needs to implement a callback function that is called by the API upon completion of reading a block of edges. Poplar uses a shared memory for interaction between its C library and the Java library that deploys the WebGraph framework.

For further details, please refer to Poplar source code repository: https://github.com/DIPSA-QUB/ParaGrapher, particularly, src/webgraph.c and src/WG*.java files.

ParaGrapher

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