Category Archives: Graptor

DIPSA at IPDPS’25

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Two of our papers were accepted at IPDPS’25.

Brian will present his work on improving the scalability of parallel molecular dynamics simulation. He has developed a novel way to reduce the scalability bottleneck that exists in the communication between those processes computing short-range forces vs those computing long-range forces. His technique discards data dependences when long-range processes are “too slow” and uses interpolation of the (slowly-varying) long-range forces to progress the computation. Stay tuned for the camera-ready copy of the paper! This work was supported by the EPSRC New Horizons project ASCCED (EP/X01794X/1).

Hans will present a parallel algorithm for the maximum clique problem. The key ideas relate to reducing the amount of work where possible, which includes delaying or avoiding the construction of fast representations of neighbour lists, early-exiting set intersection operations and algorithmic choice between maximum clique search and the complementary minimum vertex cover problem.

Addtionally, Marco will attend IPDPS’25 by virtue of a travel grant from the TCHPC/TCPP HPC student cohort programme.

SIMD Bit Twiddling Hacks

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The Bit Twiddling Hacks website collects an array of useful code fragments that implement some very specific computations very efficiently. Here we collect references to some handy code fragments for SIMD based computation.

Technical Posts

Software-Defined Floating-Point Number Formats and Their Application to Graph Processing – ICS’22

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DOI: 10.1145/3524059.3532360

This paper proposes software-defined floating-point number formats for graph processing workloads, which can improve performance in irregular workloads by reducing cache misses. Efficient arithmetic on software-defined number formats is challenging, even when based on conversion to wider, hardware-supported formats. We derive efficient conversion schemes that are tuned to the IA64 and AVX512 instruction sets.

We demonstrate that: (i) reduced-precision number formats can be applied to graph processing without loss of accuracy; (ii) conversion of floating-point values is possible
with minimal instructions; (iii) conversions are most efficient when utilizing vectorized instruction sets, specifically on IA64 processors.

Experiments on twelve real-world graph data sets demonstrate that our techniques result in speedups up to 89% for PageRank and Accelerated PageRank, and up to 35% for Single-Source Shortest Paths. The same techniques help to accelerate the integer-based maximal independent set problem by up to 262%.

Graptor Sources Published

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Finally got around to this: publishing the Graptor source code. With time passing, the code has changed quite a bit compared to that used in the paper: Graptor: efficient pull and push style vectorized graph processing. The evolution of the code has advantages: it’s faster. There are also disadvantages: not all versions and variations of the code that were experimented with can still be compiled.

The source code can be found here: https://github.com/hvdieren/graptor

There will likely be issues (errors, lack of documentation, …) as this is experimental research code. Drop me a line if you need a hand h {a dot} vandierendonck {an at} qub {another dot} ac {the last dot} uk .

Graptor: Efficient Pull and Push Style Vectorized Graph Processing

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https://doi.org/10.1145/3392717.3392753

Vectorization seeks to accelerate computation through data-level parallelism. Vectorization has been applied to graph processing, where the graph is traversed either in a push style or a pull style. As it is not well understood which style will perform better, there is a need for both vectorized push and pull style traversals. This paper is the first to present a general solution to vectorizing push style traversal. It more-over presents an enhanced vectorized pull style traversal.

Our solution consists of three components: CleanCut, a graph partitioning approach that rules out inter-thread race conditions; VectorFast, a compact graph representation that supports fast-forwarding through the edge stream; and Graptor, a domain-specific language and compiler for auto-vectorizing and optimizing graph processing codes.

Experimental evaluation demonstrates average speedups of 2.72X over Ligra, 2.46X over GraphGrind, and 2.33X over GraphIt. Graptor outperforms Grazelle, which performs vectorized pull style graph processing, by 4.05 times.