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Scientific workflows drive most modern large-scale science breakthroughs by allowing scientists to define their computations as a set of jobs executed in a given order based on their data dependencies. Workflow management systems (WMSs) have become key to automating scientific workflows-executing computational jobs and orchestrating data transfers between those jobs running on complex high-performance computing (HPC) platforms. Traditionally, WMSs use files to communicate between jobs: a job writes out files that are read by other jobs. However, HPC machines face a growing gap between their storage and compute capabilities. To address that concern, the scientific community has adopted a new approach called in situ, which bypasses costly parallel filesystem I/O operations with faster in-memory or in-network communications. When using in situ approaches, communication and computations can be interleaved. In this work, we leverage the Decaf in situ dataflow framework to accelerate task-based scientific workflows managed by the Pegasus WMS, by replacing file communications with faster MPI messaging. We propose a new execution engine that uses Decaf to manage communications within a sub-workflow (i.e., set of jobs) to optimize inter-job communications. We consider two workflows in this study: (i) a synthetic workflow that benchmarks and compares file- and MPI-based communication; and (ii) a realistic bioinformatics workflow that computes mu-tational overlaps in the human genome. Experiments show that in situ communication can improve the bioinformatics workflow execution time by 22% to 30% compared with file communication. Our results motivate further opportunities and challenges for bridging traditional WMSs with in situ frameworks.
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Poster: Minimizing Data Movement Using Distant Futures
Scientific workflows execute a series of tasks where each task may consume data as an input and produce data as an output. Within these workflows, tasks often produce intermediate results that may serve as inputs to subsequent tasks within the workflow. These results can vary in size and may need to be transported to another worker node. Data movement can become the primary bottleneck for many scientific workflows thus minimizing the cost of data movement can provide a significant performance benefit for a given workflow. Distant futures enable transfers between worker nodes, eliminating the need for intermediate results to pass through a centralized manager for future tasks invocations. Additionally, asynchronous transfers enable increased concurrency by preventing the blocking of task invocations. This poster shows the performance benefit received from the implementation of distant futures within a workflow that produces numerous intermediate results.
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- Award ID(s):
- 1931348
- PAR ID:
- 10567840
- Publisher / Repository:
- SC23: The International Conference for High Performance Computing, Networking, Storage, and Analysis
- Date Published:
- Format(s):
- Medium: X
- Institution:
- SC23: The International Conference for High Performance Computing, Networking, Storage, and Analysis
- Sponsoring Org:
- National Science Foundation
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