skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.


Title: Blaze-Tasks: A Framework for Computing Parallel Reductions over Tasks
Award ID(s):
1717635 1717515
PAR ID:
10096154
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
ACM Transactions on Architecture and Code Optimization
Volume:
15
Issue:
4
ISSN:
1544-3566
Page Range / eLocation ID:
1 to 25
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Little is known about fine scale neural dynamics that accompany rapid shifts in spatial attention in freely behaving animals, primarily because reliable indicators of attention are lacking in standard model organisms engaged in natural tasks.  The echolocating bat can serve to bridge this gap, as it exhibits robust dynamic behavioral indicators of overt spatial attention as it explores its environment.  In particular, the bat actively shifts the aim of its sonar beam to inspect objects in different directions, akin to eye movements and foveation in humans and other visually dominant animals. Further, the bat adjusts the temporal features of sonar calls to attend to objects at different distances, yielding a metric of acoustic gaze along the range axis. Thus, an echolocating bat’s call features not only convey the information it uses to probe its surroundings, but also provide fine scale metrics of auditory spatial attention in 3D natural tasks. These explicit metrics of overt spatial attention can be leveraged to uncover general principles of neural coding in the mammalian brain. 
    more » « less
  2. Users running dynamic workflows in distributed systems usually have inadequate expertise to correctly size the allocation of resources (cores, memory, disk) to each task due to the difficulty in uncovering the obscure yet important correlation between tasks and their resource consumption. Thus, users typically pay little attention to this problem of allocation sizing and either simply apply an error-prone upper bound of resource allocation to all tasks, or delegate this responsibility to underlying distributed systems, resulting in substantial waste from allocated yet unused resources. In this paper, we will first show that tasks performing different work may have significantly different resource consumption. We will then show that exploiting the heterogeneity of tasks is a desirable way to reveal and predict the relationship between tasks and their resource consumption, reduce waste from resource misallocation, increase tasks' consumption efficiency, and incentivize users' cooperation. We have developed two info-aware allocation strategies capitalizing on this characteristic and will show their effectiveness through simulations on two modern applications with dynamic workflows and five synthetic datasets of resource consumption. Our results show that info-aware strategies can cut down up to 98.7% of the total waste incurred by a best-effort strategy, and increase the efficiency in resource consumption of each task on average anywhere up to 93.9%. 
    more » « less
  3. null (Ed.)
  4. null (Ed.)