Title: Optimizing High Performance Markov Clustering for Pre-Exascale Architectures

Abstract

HipMCL is a high-performance distributed memory implementation of the popular Markov Cluster Algorithm (MCL) and can cluster large-scale networks within hours using a few thousand CPU-equipped nodes. It relies on sparse matrix computations and heavily makes use of the sparse matrix-sparse matrix multiplication kernel (SpGEMM). The existing parallel algorithms in HipMCL are not scalable to Exascale architectures, both due to their communication costs dominating the runtime at large concurrencies and also due to their inability to take advantage of accelerators that are increasingly popular. In this work, we systematically remove scalability and performance bottlenecks of HipMCL. We enable GPUs by performing the expensive expansion phase of the MCL algorithm on GPU. Additionally, we propose a CPU-GPU joint distributed SpGEMM algorithm called pipelined Sparse SUMMA and integrate a probabilistic memory requirement estimator that is fast and accurate. Furthermore, we develop a new merging algorithm for the incremental processing of partial results produced by the GPUs, which improves the overlap efficiency and the peak memory usage. We also integrate a recent and faster algorithm for performing SpGEMM on CPUs. We validate our new algorithms and optimizations with extensive evaluations. With the enabling of the GPUs and integration of new algorithms, HipMCL ismore » up to 12.4x faster, being able to cluster a network with 70 million proteins and 68 billion connections just under 15 minutes using 1024 nodes of ORNL's Summit supercomputer.« less

Authors:

Selvitopi, Oguz ^[1]; Hussain, Md Taufique ^[2]; Azad, Ariful ^[2]; Buluc, Aydin ^[1]

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+ Show Author Affiliations

1. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
2. Indiana Univ., Bloomington, IN (United States)

Publication Date:

Fri May 01 00:00:00 EDT 2020

Research Org.:

Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Org.:

USDOE Office of Science (SC)

OSTI Identifier:

1650092

Grant/Contract Number:  

AC02-05CH11231; AC05-00OR22725

Resource Type:

Accepted Manuscript

Journal Name:

Proceedings - IEEE International Parallel and Distributed Processing Symposium (IPDPS)

Additional Journal Information:

Journal Name: Proceedings - IEEE International Parallel and Distributed Processing Symposium (IPDPS); Journal Volume: 2020; Conference: 2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS), New Orleans, LA (United States), 18-22 May 2020

Publisher:

IEEE

Country of Publication:

United States

Language:

English

Subject:

97 MATHEMATICS AND COMPUTING