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Managing and preparing complex data for deep learning, a prevalent approach in large-scale data science can be challenging. Data transfer for model training also presents difficulties, impacting scientific fields like genomics, climate modeling, and astronomy. A large-scale solution like Google Pathways with a distributed execution environment for deep learning models exists but is proprietary. Integrating existing open-source, scalable runtime tools and data frameworks on high-performance computing (HPC) platforms is crucial to address these challenges. Our objective is to establish a smooth and unified method of combining data engineering and deep learning frameworks with diverse execution capabilities that can be deployed on various high-performance computing platforms, including cloud and supercomputers. We aim to support heterogeneous systems with accelerators, where Cylon and other data engineering and deep learning frameworks can utilize heterogeneous execution. To achieve this, we propose Radical-Cylon, a heterogeneous runtime system with a parallel and distributed data framework to execute Cylon as a task of Radical Pilot. We thoroughly explain Radical-Cylon’s design and development and the execution process of Cylon tasks using Radical Pilot. This approach enables the use of heterogeneous MPI-Communicators across multiple nodes. Radical-Cylon achieves better performance than Bare-Metal Cylon with minimal and constant overhead. Radical-Cylon achieves (4 15)% faster execution time than batch execution while performing similar join and sort operations with 35 million and 3.5 billion rows with the same resources. The approach aims to excel in both scientific and engineering research HPC systems while demonstrating robust performance on cloud infrastructures. This dual capability fosters collaboration and innovation within the open-source scientific research community.Not Available
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Supercharging distributed computing environments for high-performance data engineering
The data engineering and data science community has embraced the idea of using Python and R dataframes for regular applications. Driven by the big data revolution and artificial intelligence, these frameworks are now ever more important in order to process terabytes of data. They can easily exceed the capabilities of a single machine but also demand significant developer time and effort due to their convenience and ability to manipulate data with high-level abstractions that can be optimized. Therefore it is essential to design scalable dataframe solutions. There have been multiple efforts to be integrated into the most efficient fashion to tackle this problem, the most notable being the dataframe systems developed using distributed computing environments such as Dask and Ray. Even though Dask and Ray's distributed computing features look very promising, we perceive that the Dask Dataframes and Ray Datasets still have room for optimization In this paper, we present CylonFlow, an alternative distributed dataframe execution methodology that enables state-of-the-art performance and scalability on the same Dask and Ray infrastructure (superchargingthem!). To achieve this, we integrate ahigh-performance dataframesystem Cylon, which was originally based on an entirely different execution paradigm, into Dask and Ray. Our experiments show that on a pipeline of dataframe operators, CylonFlow achieves 30 × more distributed performance than Dask Dataframes. Interestingly, it also enables superior sequential performance due to leveraging the native C++ execution of Cylon. We believe the performance of Cylon in conjunction with CylonFlow extends beyond the data engineering domain and can be used to consolidate high-performance computing and distributed computing ecosystems.
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- Award ID(s):
- 2210266
- PAR ID:
- 10552193
- Publisher / Repository:
- Frontiers
- Date Published:
- Journal Name:
- Frontiers in High Performance Computing
- Volume:
- 2
- ISSN:
- 2813-7337
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3389/fhpcp.2024.1384619
