GraphPi: High Performance Graph Pattern Matching through Effective Redundancy Elimination

Shi, Tianhui; Zhai, Mingshu; Xu, Yi; Zhai, Jidong

doi:10.1109/sc41405.2020.00104

Cited by 41 publications

(48 citation statements)

References 32 publications

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“…GraphZero [40] improves AutoMine by introducing symmetry breaking to avoid overcounting. GraphPi [50] further improves GraphZero with a better performance model for redundancy elimination. Both GraphZero and GraphPi support only pattern matching, while Sandslash supports a wider range of GPM problems and also enhances performance without compromising productivity.…”

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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Section: Related Workmentioning

confidence: 99%

Sandslash

Chen

Dathathri

Gill

et al. 2021

Proceedings of the ACM International Conference on Supercomputing

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“…Unlike conventional graph processing [24,37,42,48,51], graph mining is more challenging due to the much higher algorithmic complexity. As a result, both specialized software frameworks [29,38,39,50,53,55] and hardware accelerators [11,30,57] were proposed to improve its processing efficiency.…”

Section: Introductionmentioning

confidence: 99%

FINGERS: exploiting fine-grained parallelism in graph mining accelerators

Chen

Tian

Gao

2022

Proceedings of the 27th ACM International Conference on Architectural Support for Programming Languages and Operating Systems

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Graph mining is an emerging application of high importance and also with high complexity, thus requiring efficient hardware acceleration. Current accelerator designs only utilize coarse-grained parallelism, leaving large room for further optimizations. Our key insight is to fully exploit fine-grained parallelism to overcome the existing issues of hardware underutilization, inefficient resource provision, and limited single-thread performance under imbalanced loads. Targeting pattern-aware graph mining algorithms, we first comprehensively identify and analyze the abundant fine-grained parallelism at the branch, set, and segment levels during search tree exploration and set operations. We then propose a novel graph mining accelerator, FINGERS, which effectively exploits these multiple levels of fine-grained parallelism to achieve significant performance improvements. FINGERS mainly enhances the design of each single processing element with parallel compute units for set operations, and efficient techniques for task scheduling, load balancing, and data aggregation. FINGERS outperforms the state-of-the-art design by 2.8× on average and up to 8.9× with the same chip area. We also demonstrate that different patterns and different graphs exhibit drastically different parallelism opportunities, justifying the necessity of exploiting all levels of fine-grained parallelism in FINGERS.

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“…Power optimization for the uncore subsystem is increasingly crucial because it can account for 20% of the overall CPU power consumption [11], [12], [13], and this contribution is expected to grow for future generation CPUs [14], [15], [16]. Uncore power optimization is also particularly important for emerging HPC workloads like large-scale data processing applications, which often incur extensive data communications [17], [18]. As we will show later in the paper, leaving the hardware to manage the uncore frequency often results in a significant waste of energy consumption that a more intelligent software-based optimization scheme could otherwise save.…”

Section: Introductionmentioning

confidence: 99%

Online Power Management for Multi-Cores: A Reinforcement Learning Based Approach

Wang

Zhang

Hao

et al. 2022

IEEE Trans. Parallel Distrib. Syst.

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Power and energy is the first-class design constraint for multi-core processors and is a limiting factor for future-generation supercomputers. While modern processor design provides a wide range of mechanisms for power and energy optimization, it remains unclear how software can make the best use of them. This paper presents a novel approach for runtime power optimization on modern multi-core systems. Our policy combines power capping and uncore frequency scaling to match the hardware power profile to the dynamically changing program behavior at runtime. We achieve this by employing reinforcement learning (RL) to automatically explore the energy-performance optimization space from training programs, learning the subtle relationships between the hardware power profile, the program characteristics, power consumption and program running times. Our RL framework then uses the learned knowledge to adapt the chip's power budget and uncore frequency to match the changing program phases for any new, previously unseen program. We evaluate our approach on two computing clusters by applying our techniques to 11 parallel programs that were not seen by our RL framework at the training stage. Experimental results show that our approach can reduce the system-level energy consumption by 12%, on average, with less than 3% of slowdown on the application performance. By lowering the uncore frequency to leave more energy budget to allow the processor cores to run at a higher frequency, our approach can reduce the energy consumption by up to 17% while improving the application performance by 5% for specific workloads.

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GraphPi: High Performance Graph Pattern Matching through Effective Redundancy Elimination

Cited by 41 publications

References 32 publications

Sandslash

Sandslash

FINGERS: exploiting fine-grained parallelism in graph mining accelerators

Online Power Management for Multi-Cores: A Reinforcement Learning Based Approach

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