Serverless linear algebra

Shankar, Venkatesh; Krauth, Karl; Vodrahalli, Kailas; Pu, Qifan; Recht, Benjamin; Stoica, Ion; Ragan-Kelley, Jonathan; Jonas, Eric; Venkataraman, Shivaram

doi:10.1145/3419111.3421287

Cited by 84 publications

(67 citation statements)

References 24 publications

(32 reference statements)

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“…Elasticity in DL. Recent work [41][42][43][44] has studied how to leverage compute elasticity in related workloads. NumPy-Wren [41] identies and exploits dynamic parallelism in linear algebra algorithms, including matrix multiplication (key to DL) to increase compute eciency.…”

Section: Discussion and Related Workmentioning

confidence: 99%

RubberBand

Misra

Liaw

Dunlap

et al. 2021

Proceedings of the Sixteenth European Conference on Computer Systems

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Hyperparameter tuning is essential to achieving state-of-theart accuracy in machine learning (ML), but requires substantial compute resources to perform. Existing systems primarily focus on eectively allocating resources for a hyperparameter tuning job under xed resource constraints. We show that the available parallelism in such jobs changes dynamically over the course of execution and, therefore, presents an opportunity to leverage the elasticity of the cloud.In particular, we address the problem of minimizing the nancial cost of executing a hyperparameter tuning job, subject to a time constraint. We present RubberBand-the rst framework for cost-ecient, elastic execution of hyperparameter tuning jobs in the cloud. RubberBand utilizes performance instrumentation and cloud pricing to model job completion time and cost prior to runtime, and generate a cost-ecient, elastic resource allocation plan. RubberBand is able to eciently execute this plan and realize a cost reduction of up to 2x in comparison to static allocation baselines.CCS Concepts • Computing methodologies → Distributed computing methodologies; Machine learning.

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Section: Discussion and Related Workmentioning

confidence: 99%

RubberBand

Misra

Liaw

Dunlap

et al. 2021

Proceedings of the Sixteenth European Conference on Computer Systems

Self Cite

View full text Add to dashboard Cite

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“…An edge in the graph represents a data dependency. Similar data-flow programming models exist for packet processing [38], massively parallel processing [34], and scientific computations [54].…”

Section: System Overviewmentioning

confidence: 99%

Rim

Pang

Liu

et al. 2021

Proceedings of the International Conference on Internet-of-Things Design and Implementation

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Video cameras are among the most ubiquitous sensors in the Internetof-Things. Video and audio applications, such as cross-camera activity detection, avatar extraction or language translation will, in the future, offload processing to an edge cluster of GPUs. Rim is a management system for such clusters that satisfies throughput and latency requirements of these applications, while enabling high cluster utilization. It uses coarse-grained knowledge of application structure to profile throughput of applications on resources, then uses these profiles to place applications on cluster nodes to achieve these goals. It dynamically adapts placement to load and failures. Experiments show that on maximal workloads on a testbed, Rim can satisfy requirements of all applications, but competing approaches designed for low-latency GPU execution cannot. CCS Concepts• General and reference → Performance; • Computing methodologies → Neural networks; Computer vision; Natural language processing; • Software and its engineering → Scheduling.

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“…Serverless computing is an emerging paradigm which has been shown to support a wide variety of applications such as map/reduce-style jobs [10], linear algebra computation [22], and even applications with performance guarantees [17]. Previous work has also shown how distributed ML training can be supported using serverless functions [3].…”

Section: Related Workmentioning

confidence: 99%