GraphIt: a high-performance graph DSL

Zhang, Yunming; Yang, Mengjiao; Baghdadi, Riyadh; Kamil, Shoaib; Shun, Julian; Amarasinghe, Saman

doi:10.1145/3276491

Cited by 142 publications

(93 citation statements)

References 56 publications

(50 reference statements)

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“…For the dense traversals, instead of simply mapping over the vertices with a parallel-for loop, we added an edgeaware parallelization scheme that creates tasks containing a roughly equal number of edges that are managed by the work-stealing scheduler [98]. We found this optimization to signicantly improve load balancing for hypergraphs with highly-skewed degree distributions.…”

Section: Optimizationsmentioning

confidence: 99%

Practical parallel hypergraph algorithms

Shun¹

2020

Proceedings of the 25th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming

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While there has been signicant work on parallel graph processing, there has been very surprisingly little work on highperformance hypergraph processing. This paper presents a collection of ecient parallel algorithms for hypergraph processing, including algorithms for betweenness centrality, maximal independent set, k-core decomposition, hypertrees, hyperpaths, connected components, PageRank, and single-source shortest paths. For these problems, we either provide new parallel algorithms or more ecient implementations than prior work. Furthermore, our algorithms are theoretically-ecient in terms of work and depth. To implement our algorithms, we extend the Ligra graph processing framework to support hypergraphs, and our implementations benet from graph optimizations including switching between sparse and dense traversals based on the frontier size, edge-aware parallelization, using buckets to prioritize processing of vertices, and compression. Our experiments on a 72-core machine and show that our algorithms obtain excellent parallel speedups, and are signicantly faster than algorithms in existing hypergraph processing frameworks.

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Section: Optimizationsmentioning

confidence: 99%

Practical parallel hypergraph algorithms

Shun¹

2020

Proceedings of the 25th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming

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“…Several compilers target graph operations such as breath-first-search or shortest path (e.g. [Wang et al 2016;Zhang et al 2018]). In contrast, we focus on generating high-performance traversal code for spatially coherent access to hierarchical and sparse data structures.…”

Section: Related Work 81 Array Compilersmentioning

confidence: 99%

Taichi

Hu¹,

Anderson³

et al. 2019

ACM Trans. Graph.

134

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3D visual computing data are often spatially sparse. To exploit such sparsity, people have developed hierarchical sparse data structures, such as multi-level sparse voxel grids, particles, and 3D hash tables. However, developing and using these high-performance sparse data structures is challenging, due to their intrinsic complexity and overhead. We propose Taichi , a new data-oriented programming language for efficiently authoring, accessing, and maintaining such data structures. The language offers a high-level, data structure-agnostic interface for writing computation code. The user independently specifies the data structure. We provide several elementary components with different sparsity properties that can be arbitrarily composed to create a wide range of multi-level sparse data structures. This decoupling of data structures from computation makes it easy to experiment with different data structures without changing computation code, and allows users to write computation as if they are working with a dense array. Our compiler then uses the semantics of the data structure and index analysis to automatically optimize for locality, remove redundant operations for coherent accesses, maintain sparsity and memory allocations, and generate efficient parallel and vectorized instructions for CPUs and GPUs. Our approach yields competitive performance on common computational kernels such as stencil applications, neighbor lookups, and particle scattering. We demonstrate our language by implementing simulation, rendering, and vision tasks including a material point method simulation, finite element analysis, a multigrid Poisson solver for pressure projection, volumetric path tracing, and 3D convolution on sparse grids. Our computation-data structure decoupling allows us to quickly experiment with different data arrangements, and to develop high-performance data structures tailored for specific computational tasks. With 1 <u> 1 </u> 0 th as many lines of code, we achieve 4.55× higher performance on average, compared to hand-optimized reference implementations.

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“…Besides Gluon, there are other prior works that propose various compilers for converting BSP style programs written in Galois model to Distributed systems [19] and on to GPUs [42]. Many shared memory frameworks [35,40,42,49,57,58] have also been proposed for graph analytics.…”

Section: Related Workmentioning

confidence: 99%

DisGCo

Rajendran

Nandivada

2020

ACM Trans. Archit. Code Optim.

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Graph algorithms are widely used in various applications. Their programmability and performance have garnered a lot of interest among the researchers. Being able to run these graph analytics programs on distributed systems is an important requirement. Green-Marl is a popular Domain Specific Language (DSL) for coding graph algorithms and is known for its simplicity. However, the existing Green-Marl compiler for distributed systems (Green-Marl to Pregel) can only compile limited types of Green-Marl programs (in Pregel canonical form). This severely restricts the types of parallel Green-Marl programs that can be executed on distributed systems. We present DisGCo, the first compiler to translate any general Green-Marl program to equivalent MPI program that can run on distributed systems. Translating Green-Marl programs to MPI (SPMD/MPMD style of computation, distributed memory) presents many other exciting challenges, besides the issues related to differences in syntax, as Green-Marl gives the programmer a unified view of the whole memory and allows the parallel and serial code to be intermixed. We first present the set of challenges involved in translating Green-Marl programs to MPI and then present a systematic approach to do the translation. We also present a few optimization techniques to improve the performance of our generated programs. DisGCo is the first graph DSL compiler that can handle all syntactic capabilities of a practical graph DSL like Green-Marl and generate code that can run on distributed systems. Our preliminary evaluation of DisGCo shows that our generated programs are scalable. Further, compared to the state-of-the-art DH-Falcon compiler that translates a subset of Falcon programs to MPI, our generated codes exhibit a geomean speedup of 17.32×.

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GraphIt: a high-performance graph DSL

Cited by 142 publications

References 56 publications

Practical parallel hypergraph algorithms

Practical parallel hypergraph algorithms

Taichi

DisGCo

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