skip to main content

Title: Dynamic Sizing of Continuously Divisible Jobs for Heterogeneous Resources
Many scientific applications operate on large datasets that can be partitioned and operated on concurrently.The existing approaches for concurrent execution generally rely on statically partitioned data. This static partitioning can lock performance in a sub-optimal configuration, leading to higher execution time and an inability to respond to dynamic resources.We present the Continuously Divisible Job abstraction which allows statically defined applications to have their component tasks dynamically sized responding to system behaviour. The Continuously Divisible Job abstraction defines a simple interface that dictates how work can be recursively divided, executed,and merged. Implementing this abstraction allows scientific applications to leverage dynamic job coordinators for execution.We also propose the Virtual File abstraction which allows read-only subsets of large files to be treated as separate files.In exploring the Continuously Divisible Job abstraction, two applications were implemented using the Continuously Divisible Job interface: a bioinformatics application and a high-energy physics event analysis. These were tested using an abstract job interface and several job coordinators. Comparing these against a previous static partitioning implementation we show comparable or better performance without having to make static decisions or implement complex dynamic application handling.  more » « less
Award ID(s):
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
IEEE International Conference on e-Science
Page Range / eLocation ID:
178 to 187
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. null (Ed.)
    Faceted execution is a linguistic paradigm for dynamic information-flow control with the distinguishing feature that program values may be faceted. Such values represent multiple versions or facets at once, for different security labels. This enables policy-agnostic programming: a paradigm permitting expressive privacy policies to be declared, independent of program logic. Although faceted execution prevents information leakage at runtime, it does not guarantee the absence of failure due to policy violations. By contrast with static mechanisms (such as security type systems), dynamic information-flow control permits arbitrarily expressive and dynamic privacy policies but imposes significant runtime overhead and delays discovery of any possible violations. In this paper, we present the two different abstract interpretations for faceted execution in the presence of first-class policies. We first present an abstraction which allows one to reason statically about the shape of facets at each program point. This abstraction is useful for statically proving the absence of runtime errors and eliminating runtime checks related to facets. Reasoning statically about the contents of faceted values, however, is complicated by the presence of first-class security labels, especially because abstract labels may conflate more than one runtime label. To address these issues, we also develop a more precise abstraction that relies on an analysis tracking singleton heap abstractions. We present an implementation of our coarse abstraction in Racket and demonstrate its performance on several sample programs. We conclude by showing how our precise domain can be used to verify information-flow properties. 
    more » « less
  2. In the era of big data, materials science workflows need to handle large-scale data distribution, storage, and computation. Any of these areas can become a performance bottleneck. We present a framework for analyzing internal material structures (e.g., cracks) to mitigate these bottlenecks. We demonstrate the effectiveness of our framework for a workflow performing synchrotron X-ray computed tomography reconstruction and segmentation of a silica-based structure. Our framework provides a cloud-based, cutting-edge solution to challenges such as growing intermediate and output data and heavy resource demands during image reconstruction and segmentation. Specifically, our framework efficiently manages data storage, scaling up compute resources on the cloud. The multi-layer software structure of our framework includes three layers. A top layer uses Jupyter notebooks and serves as the user interface. A middle layer uses Ansible for resource deployment and managing the execution environment. A low layer is dedicated to resource management and provides resource management and job scheduling on heterogeneous nodes (i.e., GPU and CPU). At the core of this layer, Kubernetes supports resource management, and Dask enables large-scale job scheduling for heterogeneous resources. The broader impact of our work is four-fold: through our framework, we hide the complexity of the cloud’s software stack to the user who otherwise is required to have expertise in cloud technologies; we manage job scheduling efficiently and in a scalable manner; we enable resource elasticity and workflow orchestration at a large scale; and we facilitate moving the study of nonporous structures, which has wide applications in engineering and scientific fields, to the cloud. While we demonstrate the capability of our framework for a specific materials science application, it can be adapted for other applications and domains because of its modular, multi-layer architecture. 
    more » « less
  3. Research on transaction processing has made significant progress towards improving performance of main memory multicore OLTP systems under low contention. However, these systems struggle on workloads with lots of conflicts. Partitioned databases (and variants) perform well on high contention workloads that are statically partitionable, but time-varying workloads often make them impractical. To- wards addressing this, we propose Strife—a novel transac- tion processing scheme that clusters transactions together dynamically and executes most of them without any con- currency control. Strife executes transactions in batches, where each batch is partitioned into disjoint clusters with- out any cross-cluster conflicts and a small set of residuals. The clusters are then executed in parallel with no concur- rency control, followed by residuals separately executed with concurrency control. Strife uses a fast dynamic clustering al- gorithm that exploits a combination of random sampling and concurrent union-find data structure to partition the batch online, before executing it. Strife outperforms lock-based and optimistic protocols by up to 2× on high contention workloads. While Strife incurs about 50% overhead relative to partitioned systems in the statically partitionable case, it performs 2× better when such static partitioning is not possible and adapts to dynamically varying workloads. 
    more » « less
  4. null (Ed.)
    The proliferation of GPS-enabled devices has led to the development of numerous location-based services. These services need to process massive amounts of streamed spatial data in real-time. The current scale of spatial data cannot be handled using centralized systems. This has led to the development of distributed spatial streaming systems. Existing systems are using static spatial partitioning to distribute the workload. In contrast, the real-time streamed spatial data follows non-uniform spatial distributions that are continuously changing over time. Distributed spatial streaming systems need to react to the changes in the distribution of spatial data and queries. This article introduces SWARM, a lightweight adaptivity protocol that continuously monitors the data and query workloads across the distributed processes of the spatial data streaming system and redistributes and rebalances the workloads as soon as performance bottlenecks get detected. SWARM is able to handle multiple query-execution and data-persistence models. A distributed streaming system can directly use SWARM to adaptively rebalance the system’s workload among its machines with minimal changes to the original code of the underlying spatial application. Extensive experimental evaluation using real and synthetic datasets illustrate that, on average, SWARM achieves 2 improvement in throughput over a static grid partitioning that is determined based on observing a limited history of the data and query workloads. Moreover, SWARM reduces execution latency on average 4 compared with the other technique. 
    more » « less
  5. Many applications can benefit from data that increases performance but is not required for correctness (commonly referred to as soft state). Examples include cached data from backend web servers and memoized computations in data analytics systems. Today's systems generally statically limit the amount of memory they use for storing soft state in order to prevent unbounded growth that could exhaust the server's memory. Static provisioning, however, makes it difficult to respond to shifts in application demand for soft state and can leave significant amounts of memory idle. Existing OS kernels can only spend idle memory on caching disk blocks—which may not have the most utility—because they do not provide the right abstractions to safely allow applications to store their own soft state. To effectively manage and dynamically scale soft state, we propose soft memory, an elastic virtual memory abstraction with unmap-and-reconstruct semantics that makes it possible for applications to use idle memory to store whatever soft state they choose while guaranteeing both safety and efficiency. We present Midas, a soft memory management system that contains (1) a runtime that is linked to each application to manage soft memory objects and (2) OS kernel support that coordinates soft memory allocation between applications to maximize their performance. Our experiments with four real-world applications show that Midas can efficiently and safely harvest idle memory to store applications' soft state, delivering near-optimal application performance and responding to extreme memory pressure without running out of memory. 
    more » « less