• Documents
  • Authors
  • Tables
  • Log in
  • Sign up
  • MetaCart
  • DMCA
  • Donate

CiteSeerX logo

Advanced Search Include Citations

Tools

Sorted by:
Try your query at:
Semantic Scholar Scholar Academic
Google Bing DBLP
Results 1 - 10 of 1,186
Next 10 →

Runtime power monitoring in high-end processors: Methodology and empirical data

by Canturk Isci, Margaret Martonosi , 2003
"... With power dissipation becoming an increasingly vexing problem across many classes of computer systems, measuring power dissipation of real, running systems has become crucial for hardware and software system research and design. Live power measurements are imperative for studies requiring execution ..."
Abstract - Cited by 199 (4 self) - Add to MetaCart
CPU subunits over minutes of SPEC2000 and desktop workload execution. As an example application, we use the generated component power breakdowns to identify program power phase behavior. Overall, this paper demonstrates a processor power measurement and estimation methodology and also gives

Evaluating MapReduce for multi-core and multiprocessor systems

by Colby Ranger, Ramanan Raghuraman, Arun Penmetsa, Gary Bradski, Christos Kozyrakis - In HPCA ’07: Proceedings of the 13th International Symposium on High-Performance Computer Architecture , 2007
"... This paper evaluates the suitability of the MapReduce model for multi-core and multi-processor systems. MapReduce was created by Google for application development on data-centers with thousands of servers. It allows programmers to write functional-style code that is automatically parallelized and s ..."
Abstract - Cited by 256 (3 self) - Add to MetaCart
and scheduled in a distributed system. We describe Phoenix, an implementation of MapReduce for shared-memory systems that includes a programming API and an efficient runtime system. The Phoenix runtime automatically manages thread creation, dynamic task scheduling, data partitioning, and fault tolerance across

Modeling User Runtime Estimates

by Dan Tsafrir, Yoav Etsion, Dror G. Feitelson - In 11th Workshop on Job Scheduling Strategies for Parallel Processing (JSSPP 2005 , 2005
"... Abstract. User estimates of job runtimes have emerged as an important component of the workload on parallel machines, and can have a significant impact on how a scheduler treats different jobs, and thus on overall performance. It is therefore highly desirable to have a good model of the relationship ..."
Abstract - Cited by 42 (6 self) - Add to MetaCart
Abstract. User estimates of job runtimes have emerged as an important component of the workload on parallel machines, and can have a significant impact on how a scheduler treats different jobs, and thus on overall performance. It is therefore highly desirable to have a good model

Are User Runtime Estimates Inherently Inaccurate?

by Cynthia Bailey Lee, Yael Schwartzman, Jennifer Hardy, Allan Snavely , 2004
"... Computer system batch schedulers typically require information from the user upon job submission, including a runtime estimate. Inaccuracy of these runtime estimates, relative to the actual runtime of the job, has been well documented and is a perennial problem mentioned in the job scheduling lit ..."
Abstract - Cited by 60 (1 self) - Add to MetaCart
Computer system batch schedulers typically require information from the user upon job submission, including a runtime estimate. Inaccuracy of these runtime estimates, relative to the actual runtime of the job, has been well documented and is a perennial problem mentioned in the job scheduling

Lightweight Run-Time Code Generation

by Mark Leone, Peter Lee - Department of Computer Science, University of Melbourne , 1994
"... Run-time code generation is an alternative and complement to compile-time program analysis and optimization. Static analyses are inherently imprecise because most interesting aspects of run-time behavior are uncomputable. By deferring aspects of compilation to run time, more precise information abou ..."
Abstract - Cited by 55 (5 self) - Add to MetaCart
Run-time code generation is an alternative and complement to compile-time program analysis and optimization. Static analyses are inherently imprecise because most interesting aspects of run-time behavior are uncomputable. By deferring aspects of compilation to run time, more precise information

A General Predictive Performance Model for Wavefront Algorithms on Clusters of SMPs

by Adolfy Hoisie Olaf , 2000
"... this paper is to capture this tradeoff and the influence of the blocking parameters on the overall runtime of the application. ..."
Abstract - Add to MetaCart
this paper is to capture this tradeoff and the influence of the blocking parameters on the overall runtime of the application.

Task Decomposition, Dynamic Role Assignment, and Low-Bandwidth Communication for Real-Time Strategic Teamwork

by Peter Stone, Manuela Veloso - ARTIFICIAL INTELLIGENCE , 1999
"... Multi-agent domains consisting of teams of agents that need to collaborate in an adversarial environment offer challenging research opportunities. In this article, we introduce periodic team synchronization (PTS) domains as time-critical environments in which agents act autonomously with low commu ..."
Abstract - Cited by 220 (20 self) - Add to MetaCart
can change formations dynamically, according to pre-defined triggers to be evaluated at run-time. This flexibility increases the performance of the overall team. Our teamwork structure further includes pre-planning for frequent situations. Second, the novel communication method is designed for use

RUNTIME ESTIMATION OF PARALLEL APPLICATIONS IN COMPUTATIONAL GRIDS

by P. Hu, Z. Qiao, L. F. Sun, E. C. Ifeachor
"... The purposes of runtime prediction in grid computing are to provide quality information in order to deliver user-required quality of service and to offer an efficient resource sharing environment. The aim of this paper is to investigate efficient methods for runtime estimation of parallel applicatio ..."
Abstract - Add to MetaCart
applications in computational grids from the view of both computation and communication costs. The main contributions of the paper are two-fold. First, we present a learning-based method to predict computation time cost. Second, we propose a new mathematical model to predict overall runtime for executing

Memory Safety Without Runtime Checks or Garbage Collection

by Dinakar Dhurjati, Sumant Kowshik, Vikram Adve, Chris Lattner - In ACM SIGPLAN 2003 Conference on Languages, Compilers, and Tools for Embedded Systems (LCTES’2003 , 2003
"... Traditional approaches to enforcing memory safety of programs rely heavily on runtime checks of memory accesses and on garbage collection, both of which are unattractive for embedded applications. The long-term goal of our work is to enable 100% static enforcement of memory safety for embedded progr ..."
Abstract - Cited by 62 (8 self) - Add to MetaCart
Traditional approaches to enforcing memory safety of programs rely heavily on runtime checks of memory accesses and on garbage collection, both of which are unattractive for embedded applications. The long-term goal of our work is to enable 100% static enforcement of memory safety for embedded

Object Distribution in Orca using Compile-Time and Run-Time Techniques

by Henri E. Bal, M. Frans Kaashoek , 1993
"... Orca is a language for parallel programming on distributed systems. Communication in Orca is based on shared data-objects, which is a form of distributed shared memory. The performance of Orca programs depends strongly on how shared dataobjects are distributed among the local physical memories of th ..."
Abstract - Cited by 81 (20 self) - Add to MetaCart
of the processors. This paper studies a new and efficient solution to this problem, based on an integration of compile-time and run-time techniques. The Orca compiler has been extended to determine the access patterns of processes to shared objects. The compiler passes a summary of this information to the run-time
Next 10 →
Results 1 - 10 of 1,186
Powered by: Apache Solr
  • About CiteSeerX
  • Submit and Index Documents
  • Privacy Policy
  • Help
  • Data
  • Source
  • Contact Us

Developed at and hosted by The College of Information Sciences and Technology

© 2007-2019 The Pennsylvania State University