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Developing server applications that offload computation and data to a NIC accelerator is laborious because one has to explore the design space of decisions about data placement and caching; partitioning of code and its parallelism; and communication strategies between program components across devices. We propose programming abstractions for NIC-accelerated applications, balancing the ease of developing a correct application and the ability to refactor it to explore different design choices. The design space includes semantic changes as well as variations on parallelization and program-to-resource mapping. Our abstractions include logical and physical queues and a construct for mapping the former onto the latter; global per-packet state; a remote caching construct; and an interface to external application code. We develop Floem, a programming system that provides these abstractions, and show that the system helps explore a space of NIC-offloading designs for real-world applications, including a key-value store and a distributed real-time data analytics system, improving throughput by 1.3--3.6x.
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Data-trace types for distributed stream processing systems
Distributed architectures for efficient processing of streaming data are increasingly critical to modern information processing systems. The goal of this paper is to develop type-based programming abstractions that facilitate correct and efficient deployment of a logical specification of the desired computation on such architectures. In the proposed model, each communication link has an associated type specifying tagged data items along with a dependency relation over tags that captures the logical partial ordering constraints over data items. The semantics of a (distributed) stream processing system is then a function from input data traces to output data traces, where a data trace is an equivalence class of sequences of data items induced by the dependency relation. This data-trace transduction model generalizes both acyclic synchronous data-flow and relational query processors, and can specify computations over data streams with a rich variety of partial ordering and synchronization characteristics. We then describe a set of programming templates for data-trace transductions: abstractions corresponding to common stream processing tasks. Our system automatically maps these high-level programs to a given topology on the distributed implementation platform Apache Storm while preserving the semantics. Our experimental evaluation shows that (1) while automatic parallelization deployed by existing systems may not preserve semantics, particularly when the computation is sensitive to the ordering of data items, our programming abstractions allow a natural specification of the query that contains a mix of ordering constraints while guaranteeing correct deployment, and (2) the throughput of the automatically compiled distributed code is comparable to that of hand-crafted distributed implementations.
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- PAR ID:
- 10111017
- Date Published:
- Journal Name:
- 40th ACM SIGPLAN Conference on Programming Language Design and Implementation
- Page Range / eLocation ID:
- 670 to 685
- 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/10.1145/3314221.3314580
