Fast Triangle Counting Using Cilk

Yaşar, Abdurrahman; Rajamanickam, Sivasankaran; Wolf, Michael M.; Berry, Jonathan W.; Çatalyürek, Ümit V.

doi:10.1109/hpec.2018.8547563

Cited by 23 publications

(23 citation statements)

References 22 publications

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“…Later, [32], [33], [34] use bitmap to represent the hash bucket which still suffers from high cache pressure. We also notice that Yacsar et al [35], [36], [37], [38] switch between dense and sparse representations of a hash table in their matrix-multiplication effort which is, however, complex in nature.…”

Section: Related Work and Challengesmentioning

confidence: 94%

TRUST: Triangle Counting Reloaded on GPUs

Pandey,

Wang,

Zhong

et al. 2021

Preprint

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Triangle counting is a building block for a wide range of graph applications. Traditional wisdom suggests that i) hashing is not suitable for triangle counting, ii) edge-centric triangle counting beats vertex-centric design, and iii) communication-free and workload balanced graph partitioning is a grand challenge for triangle counting. On the contrary, we advocate that i) hashing can help the key operations for scalable triangle counting on Graphics Processing Units (GPUs), i.e., list intersection and graph partitioning, ii) vertex-centric design reduces both hash table construction cost and memory consumption, which is limited on GPUs. In addition, iii) we exploit graph and workload collaborative, and hashing-based 2D partitioning to scale vertex-centric triangle counting over 1,000 GPUs with sustained scalability. In this work, we present TRUST which performs triangle counting with the hash operation and vertex-centric mechanism at the core. To the best of our knowledge, TRUST is the first work that achieves over one trillion Traversed Edges Per Second (TEPS) rate for triangle counting.

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Section: Related Work and Challengesmentioning

confidence: 94%

TRUST: Triangle Counting Reloaded on GPUs

Pandey,

Wang,

Zhong

et al. 2021

Preprint

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“…For example, the Kokkos Kernels SpGEMM implementation led to highly optimized implementation of the triangle counting kernel (Wolf et al, 2017). Since then, there are other fast implementations of triangle counting kernels based on Cilk (Yaşar et al, 2018) and using both CPUs and GPUs (Yasar et al, 2019) and distributed memory systems (Acer et al, 2019).…”

Section: Algebraic Approaches For Graph Algorithms and Combinatorial Problemsmentioning

confidence: 99%

EXAGRAPH: Graph and combinatorial methods for enabling exascale applications

Acer

Azad

Boman

et al. 2021

The International Journal of High Performance Computing Applica

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Combinatorial algorithms in general and graph algorithms in particular play a critical enabling role in numerous scientific applications. However, the irregular memory access nature of these algorithms makes them one of the hardest algorithmic kernels to implement on parallel systems. With tens of billions of hardware threads and deep memory hierarchies, the exascale computing systems in particular pose extreme challenges in scaling graph algorithms. The codesign center on combinatorial algorithms, ExaGraph, was established to design and develop methods and techniques for efficient implementation of key combinatorial (graph) algorithms chosen from a diverse set of exascale applications. Algebraic and combinatorial methods have a complementary role in the advancement of computational science and engineering, including playing an enabling role on each other. In this paper, we survey the algorithmic and software development activities performed under the auspices of ExaGraph from both a combinatorial and an algebraic perspective. In particular, we detail our recent efforts in porting the algorithms to manycore accelerator (GPU) architectures. We also provide a brief survey of the applications that have benefited from the scalable implementations of different combinatorial algorithms to enable scientific discovery at scale. We believe that several applications will benefit from the algorithmic and software tools developed by the ExaGraph team.

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“…GPM Algorithms: There are numerous hand-optimized GPM applications targeting various platforms. For TC, there are parallel solvers on multicore CPUs [19,51,60,63], distributed CPUs [24,46,54], and GPUs [28,29,45]. kClist [17] is a parallel k-CL algorithm derived from [15].…”

Section: Related Workmentioning

confidence: 99%

Sandslash

Chen

Dathathri

Gill

et al. 2021

Proceedings of the ACM International Conference on Supercomputing

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Graph pattern mining (GPM) is a key building block in diverse applications, including bioinformatics, chemical engineering, social network analysis, recommender systems and security. Existing GPM frameworks either provide high-level interfaces for productivity at the cost of expressiveness or provide expressive low-level interfaces at the cost of increased programming complexity. They also lack the flexibility to explore combinations of optimizations to achieve performance competitive with hand-optimized applications.We present Sandslash, an in-memory graph pattern mining framework that uses a novel programming interface to support productive, expressive, and efficient GPM on large graphs. Sandslash provides a high-level API that only needs a specification of the GPM problem from the user, and the system implements fast subgraph enumeration, provides efficient data structures, and applies high-level optimizations automatically. To achieve performance competitive with expert-optimized implementations, Sandslash also provides a low-level API that allows users to express algorithm-specific optimizations. This enables Sandslash to support both high-productivity and high-efficiency without losing expressiveness. We evaluate Sandslash using five GPM applications and a wide range of real-world graphs. Experimental results demonstrate that applications written using Sandslash's high-level or low-level API outperform those in state-of-the-art GPM systems Au-toMine, Pangolin, and Peregrine on average by 13.8×, 7.9×, and 5.4×, respectively. We also show that these Sandslash applications outperform expert-optimized GPM implementations by 2.3× on average with less programming effort. CCS Concepts• Software and its engineering → Application specific development environments.

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Fast Triangle Counting Using Cilk

Cited by 23 publications

References 22 publications

TRUST: Triangle Counting Reloaded on GPUs

TRUST: Triangle Counting Reloaded on GPUs

EXAGRAPH: Graph and combinatorial methods for enabling exascale applications

Sandslash

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