Speculative Slot Reservation: Enforcing Service Isolation for Dependent Data-Parallel Computations

2017 ◽  
Author(s):  
Chen Chen ◽  
Wei Wang ◽  
Bo Li
Keyword(s):  
Parallel Computations ◽  
Dependent Data ◽  
Data Parallel

scholarly journals GUIDELINES FOR DATA-PARALLEL CYCLE-STEALING IN NETWORKS OF WORKSTATIONS II: ON MAXIMIZING GUARANTEED OUTPUT

2000 ◽  
Vol 11 (01) ◽  
pp. 183-204 ◽  
Author(s):  
ARNOLD L. ROSENBERG
Keyword(s):  
Parallel Computations ◽  
The Other ◽  
Work Output ◽  
Change Strategy ◽  
Work In Progress ◽  
Data Parallel ◽  
Other Hand ◽  
The One ◽  
Cycle Stealing ◽  
Low Order

We derive efficient guidelines for scheduling data-parallel computations within a draconian mode of cycle-stealing in networks of workstations. In this computing regimen, (the owner of) workstation A contracts with (the owner of) workstation B to take control of B's processor for a guaranteed total of U time units, punctuated by up to some prespecified number p of interrupts which kill any work A has in progress on B. On the one hand, the high overhead — of c time units — for setting up the communications that supply workstation B with work and receive its results recommends that A communicate with B infrequently, supplying B with large amounts of work each time. On the other hand, the risk of losing work in progress when workstation B is interrupted recommends that A supply B with a long sequence of small bundles of work. In this paper, we derive two sets of scheduling guidelines that balance these conflicting pressures in a way that optimizes, up to low-order additive terms, the amount of work that A is guaranteed to accomplish during the cycle-stealing opportunity. Our non-adaptive guidelines, which employ a single fixed strategy until all p interrupts have occurred, produce schedules that achieve at least [Formula: see text] units of work. Our adaptive guidelines, which change strategy after each interrupt, produce schedules that achieve at least [Formula: see text] (low-order terms) units of work. By deriving the theoretical underpinnings of our guidelines, we show that our non-adaptive schedules are optimal in guaranteed work-output and that our adaptive schedules are within low-order additive terms of being optimal.

Optimal dynamic remapping of data parallel computations

1990 ◽  
Vol 39 (2) ◽  
pp. 206-219 ◽  
Author(s):  
D.M. Nicol ◽  
P.F. Reynolds
Keyword(s):  
Parallel Computations ◽  
Data Parallel ◽  
Optimal Dynamic
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