Amir H. Bahmani

Works (3)

Updated: July 5th, 2023 15:38

2017 journal article

Scalable communication event tracing via clustering

JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING, 109, 230–244.

By: A. Bahmani n & F. Mueller n

author keywords: Clustering algorithms; Programming techniques; Concurrent programming; Performance measurement
TL;DR: An adaptive clustering algorithm for large-scale applications called ACURDION is devised that traces the MPI communication of code with O(log P) time complexity and improves trace scalability and automation over prior approaches. (via Semantic Scholar)
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Added: August 6, 2018

2016 journal article

Efficient clustering for ultra-scale application tracing

JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING, 98, 25–39.

By: A. Bahmani n & F. Mueller n

author keywords: Clustering algorithms; Programming techniques; Concurrent programming; Performance measurement
TL;DR: This work contributes a fast, scalable, signature-based clustering algorithm that clusters processes exhibiting similar execution behavior that combines low overhead at the clustering level with l o g ( P ) time complexity, and it splits the merge process to make tracing suitable for extreme-scale computing. (via Semantic Scholar)
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2016 article

SparkScore: Leveraging Apache Spark for Distributed Genomic Inference

2016 IEEE 30TH INTERNATIONAL PARALLEL AND DISTRIBUTED PROCESSING SYMPOSIUM WORKSHOPS (IPDPSW), pp. 435–442.

By: A. Bahmani n, A. Sibley *, M. Parsian*, K. Owzar * & F. Mueller n

TL;DR: SparkScore, a set of distributed computational algorithms implemented in Apache Spark, is proposed to leverage the embarrassingly parallel nature of genomic resampling inference on the basis of the efficient score statistics and harnesses the fault-tolerant features of Spark. (via Semantic Scholar)
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