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We propose Clamor, a functional cluster computing framework that adds support for fine-grained, transparent access to global variables for distributed, data-parallel tasks. Clamor targets workloads that perform sparse accesses and updates within the bulk synchronous parallel execution model, a setting where the standard technique of broadcasting global variables is highly inefficient. Clamor implements a novel dynamic replication mechanism in order to enable efficient access to popular data regions on the fly, and tracks finegrained dependencies in order to retain the lineage-based fault tolerance model of systems like Spark. Clamor can integrate with existing Rust and C++ libraries to transparently distribute programs on the cluster. We show that Clamor is competitive with Spark in simple functional workloads and can improve performance significantly compared to custom systems on workloads that sparsely access large global variables: from 5x for sparse logistic regression to over 100x on distributed geospatial queries.
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Terminus: A Programmable Accelerator for Read and Update Operations on Sparse Data Structures
Sparse data structures like hash tables, trees, or compressed tensors are ubiquitous, but operations on these structures are expensive and inefficient on current systems. Prior work has proposed hardware acceleration for these operations, but these techniques have two key shortcomings: they limit the types of data structures they support, and they focus on reads but do not support fine-grained updates to these structures. We present Terminus, a programmable accelerator for read and update operations on sparse data structures. Terminus extends each general-purpose core with a programmable dataflow engine capable of accelerating a wide range of structures and operations. Terminus engines are flexible yet simple, as they focus on common operations and defer rare, complex ones to cores. Terminus features a simple concurrency control mechanism based on address ranges that enables safe updates while preserving parallelism. We evaluate Terminus on serial and parallel benchmarks on a wide range of sparse data structures. Terminus improves performance by gmean 7.4x over a CPU baseline, showing that Terminus can accelerate fine-grained reads and writes that were previously not possible in prior accelerators for sparse structures.
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- Award ID(s):
- 2217099
- PAR ID:
- 10638827
- Publisher / Repository:
- IEEE
- Date Published:
- Page Range / eLocation ID:
- 1233 to 1246
- 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
- Conference Paper:
- https://doi.org/10.1109/MICRO61859.2024.00092
