scholarly journals A GPU Scheduling Framework to Accelerate Hyper-Parameter Optimization in Deep Learning Clusters

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 350
Author(s):  
Jaewon Son ◽  
Yonghyuk Yoo ◽  
Khu-rai Kim ◽  
Youngjae Kim ◽  
Kwonyong Lee ◽  
...  
Keyword(s):  
Deep Learning ◽  
Parameter Optimization ◽  
Round Robin ◽  
Time Sharing ◽  
Scheduling Policies ◽  
Scheduling Policy ◽  
Minimal Time ◽  
Gpu Scheduling ◽  
Early Phases

This paper proposes Hermes, a container-based preemptive GPU scheduling framework for accelerating hyper-parameter optimization in deep learning (DL) clusters. Hermes accelerates hyper-parameter optimization by time-sharing between DL jobs and prioritizing jobs with more promising hyper-parameter combinations. Hermes’s scheduling policy is grounded on the observation that good hyper-parameter combinations converge quickly in the early phases of training. By giving higher priority to fast-converging containers, Hermes’s GPU preemption mechanism can accelerate training. This enables users to find optimal hyper-parameters faster without losing the progress of a container. We have implemented Hermes over Kubernetes and compared its performance against existing scheduling frameworks. Experiments show that Hermes reduces the time for hyper-parameter optimization up to 4.04 times against previously proposed scheduling policies such as FIFO, round-robin (RR), and SLAQ, with minimal time-sharing overhead.

scholarly journals REAL-TIME SCHEDULING POLICY IN EMBEDDED SYSTEM DOMAIN: A FRAME WORK

2013 ◽  
pp. 124-129
Author(s):  
ASHIS KUMAR MISHRA ◽  
ZULFIKHAR AHMAD ◽  
YOGAMAYA MOHAPATRA ◽  
ANIL KUMAR MISHRA
Keyword(s):  
Embedded System ◽  
Real Time ◽  
Classical Problem ◽  
Time Sharing ◽  
Scheduling Policies ◽  
Real Time Scheduling ◽  
Cpu Scheduling ◽  
Scheduling Policy ◽  
Time Scheduling ◽  
Time Systems

Scheduling a sequence of jobs released over time when the processing time of a job is only known at its completion is a classical problem in CPU scheduling in time-sharing and real time operating system. We discuss here different scheduling techniques used in Real-Time systems. Even if there are several scheduling policies, the preemptive scheduling policies hold promising results. In this paper we have done an extensive survey on various scheduling algorithms. We are extracting the positive characteristics of each scheduling and placed it on this paper.

scholarly journals Optimal scheduling policies in time sharing service systems

1995 ◽  
Vol 22 (10-12) ◽  
pp. 247-259 ◽  
Author(s):  
M. Ohnishi ◽  
H. Maeda ◽  
T. Ibaraki
Keyword(s):  
Service Systems ◽  
Optimal Scheduling ◽  
Time Sharing ◽  
Scheduling Policies

scholarly journals Space profiling for parallel functional programs

2010 ◽  
Vol 20 (5-6) ◽  
pp. 417-461 ◽  
Author(s):  
DANIEL SPOONHOWER ◽  
GUY E. BLELLOCH ◽  
ROBERT HARPER ◽  
PHILLIP B. GIBBONS
Keyword(s):  
Resource Use ◽  
Source Code ◽  
Semantic Space ◽  
Runtime System ◽  
Scheduling Policies ◽  
Use Patterns ◽  
Scheduling Policy ◽  
Standard Ml ◽  
Cost Semantics ◽  
The Impact

AbstractWe present a semantic space profiler for parallel functional programs. Building on previous work in sequential profiling, our tools help programmers to relate runtime resource use back to program source code. Unlike many profiling tools, our profiler is based on a cost semantics. This provides a means to reason about performance without requiring a detailed understanding of the compiler or runtime system. It also provides a specification for language implementers. This is critical in that it enables us to separate cleanly the performance of the application from that of the language implementation. Some aspects of the implementation can have significant effects on performance. Our cost semantics enables programmers to understand the impact of different scheduling policies while hiding many of the details of their implementations. We show applications where the choice of scheduling policy has asymptotic effects on space use. We explain these use patterns through a demonstration of our tools. We also validate our methodology by observing similar performance in our implementation of a parallel extension of Standard ML.

scholarly journals Deep Learning Hyper Parameter Optimization for Video Analytic in Centralized System

2019 ◽  
Vol 9 (1) ◽  
pp. 7300-7305
Keyword(s):  
Deep Learning ◽  
Parameter Optimization ◽  
Distributed Storage ◽  
Parameter Tuning ◽  
Video Object ◽  
Video Streams ◽  
Scientific Model ◽  
Wide Range ◽  
Centralized System

A framework to perform video examination is proposed utilizing a powerfully tuned convolutional arrange. Recordings are gotten from distributed storage, preprocessed, and a model for supporting order is created on these video streams utilizing cloud-based framework. A key spotlight in this paper is on tuning hyper-parameters related with the profound learning calculation used to build the model. We further propose a programmed video object order pipeline to approve the framework. The scientific model used to help hyper-parameter tuning improves execution of the proposed pipeline, and results of different parameters on framework's presentation is analyzed. Along these lines, the parameters that contribute toward the most ideal presentation are chosen for the video object order pipeline. Our examination based approval uncovers an exactness and accuracy of 97% and 96%, separately. The framework demonstrated to be adaptable, strong, and adjustable for a wide range of utilizations.

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