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1. Enabling Exploratory Large Scale Graph Analytics through Arkouda

   https://doi.org/10.1109/HPEC49654.2021.9622860  

   Du, Zhihui ; Rodriguez, Oliver Alvarado ; Bader, David A. ( September 2021 , The 25th Annual IEEE High Performance Extreme Computing Conference (HPEC))

   Exploratory graph analytics helps maximize the informational value from a graph. However, the increasing graph size makes it impossible for existing popular exploratory data analysis tools to handle dozens-of-terabytes or even larger data sets in the memory of a common laptop/personal computer. Arkouda is a framework under early-development that brings together the productivity of Python at the user side with the high-performance of Chapel at the server side. In this paper, we present preliminary work on overcoming the memory limit and high performance computing coding roadblock for high level Python users to perform large graph analysis. A simple and succinct graph data structure design and implementation at both the Python front-end and the Chapel back-end in the Arkouda framework are provided. A typical graph algorithm, Breadth-First Search (BFS), is used to show how we can use Chapel to develop high performance parallel graph algorithm productively. Two Chapel-based parallel Breadth-First Search (BFS) algorithms, one high level version and one corresponding low level version, have been implemented in Arkouda to support analyzing large graphs. Multiple graph benchmarks are used to evaluate the performance of the provided graph algorithms. Ex- perimental results show that we can optimize the performance by tuning the selection of different Chapel high level data structures and parallel constructs. Our code is open source and available from GitHub (https://github.com/Bader-Research/arkouda). 

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2. Large Scale String Analytics in Arkouda

   https://doi.org/10.1109/HPEC49654.2021.9622810  

   Du, Zhihui ; Rodriguez, Oliver Alvarado ; Bader, David A. ( September 2021 , The 25th Annual IEEE High Performance Extreme Computing Conference (HPEC))

   Large scale data sets from the web, social networks, and bioinformatics are widely available and can often be represented by strings and suffix arrays are highly efficient data structures enabling string analysis. But, our personal devices and corresponding exploratory data analysis (EDA) tools cannot handle big data sets beyond the local memory. Arkouda is a framework under early development that brings together the productivity of Python at the user side with the high-performance of Chapel at the server-side. In this paper, an efficient suffix array data structure design and integration method are given first. A suffix array algorithm library integration method instead of one single suffix algorithm is presented to enable runtime performance optimization in Arkouda since different suffix array algorithms may have very different practical performances for strings in various applications. A parallel suffix array construction algorithm framework is given to further exploit hierarchical parallelism on multiple locales in Chapel. A corresponding benchmark is developed to evaluate the feasibility of the provided suffix array integration method and measure the end-to-end performance. Experimental results show that the proposed solution can provide data scientists an easy and efficient method to build suffix arrays with high performance in Python. All our codes are open source and available from GitHub (https://github.com/Bader-Research/arkouda/tree/string-suffix-array-functionality). 

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3. Parallel Longest Common SubSequence Analysis In Chapel

   Vahidi, Soroush ; Schieber, Baruch ; Du, Zhihui ; Bader, David ( September 2023 , The 27th Annual IEEE High Performance Extreme Computing Conference (HPEC))

   One of the most critical problems in the field of string algorithms is the longest common subsequence problem (LCS). The problem is NP-hard for an arbitrary number of strings but can be solved in polynomial time for a fixed number of strings. In this paper, we select a typical parallel LCS algorithm and integrate it into our large-scale string analysis algorithm library to support different types of large string analysis. Specifically, we take advantage of the high-level parallel language, Chapel, to integrate Lu and Liu’s parallel LCS algorithm into Arkouda, an open-source framework. Through Arkouda, data scientists can easily handle large string analytics on the back-end high-performance computing resources from the front-end Python interface. The Chapel-enabled parallel LCS algorithm can identify the longest common subsequences of two strings, and experimental results are given to show how the number of parallel resources and the length of input strings can affect the algorithm’s performance. 

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4. Parallel Longest Common SubSequence Analysis In Chapel

   https://doi.org/10.1109/HPEC58863.2023.10363472  

   Vahidi, Soroush ; Schieber, Baruch ; Du, Zhihui ; Bader, David A ( September 2023 , IEEE)

   One of the most critical problems in the field of string algorithms is the longest common subsequence problem (LCS). The problem is NP-hard for an arbitrary number of strings but can be solved in polynomial time for a fixed number of strings. In this paper, we select a typical parallel LCS algorithm and integrate it into our large-scale string analysis algorithm library to support different types of large string analysis. Specifically, we take advantage of the high-level parallel language, Chapel, to integrate Lu and Liu’s parallel LCS algorithm into Arkouda, an open-source framework. Through Arkouda, data scientists can easily handle large string analytics on the back-end high-performance computing resources from the front-end Python interface. The Chapel-enabled parallel LCS algorithm can identify the longest common subsequences of two strings, and experimental results are given to show how the number of parallel resources and the length of input strings can affect the algorithm’s performance. 

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5. High-Performance Truss Analysis in Arkouda

   Zhihui Du ; Joseph Patchett ; Oliver Alvarado Rodriguez ; David A. Bader ( December 2022 , The 29th IEEE International Conference on High Performance Computing, Data, and Analytics (HiPC))

   In graph analytics, a truss is a cohesive subgraph based on the number of triangles supporting each edge. It is widely used for community detection applications such as social networks and security analysis, and the performance of truss analytics highly depends on its triangle counting method. This paper proposes a novel triangle counting kernel named Minimum Search (MS). Minimum Search can select two smaller adjacency lists out of three and uses fine-grained parallelism to improve the performance of triangle counting. Then, two basic algorithms, MS-based triangle counting, and MS-based support updating are developed. Based on the novel triangle counting kernel and the two basic algorithms above, three fundamental parallel truss analytics algorithms are designed and implemented to enable different kinds of graph truss analysis. These truss algorithms include an optimized K-Truss algorithm, a Max-Truss algorithm, and a Truss Decomposition algorithm. Moreover, all proposed algorithms have been implemented in the parallel language Chapel and integrated into an open-source framework, Arkouda. Through Arkouda, data scientists can efficiently conduct graph analysis through an easy-to-use Python interface and handle large-scale graph data in powerful back-end computing resources. Experimental results show that the proposed methods can significantly improve the performance of truss analysis on real-world graphs compared with the existing and widely adopted list intersection-based method. The implemented code is publicly available from GitHub (https://github.com/Bears-R-Us/arkoudanjit). } 

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