NDLI: PHANTOM: predicting performance of parallel applications on large-scale parallel machines using a single node

Please wait, while we are loading the content...

Structure-driven optimizations for amorphous data-parallel programs

Is transactional programming actually easier?

Extreme scale computing: challenges and opportunities

An adaptive performance modeling tool for GPU architectures

Load balancing on speed

Lazy binary-splitting: a run-time adaptive work-stealing scheduler

Does cache sharing on modern CMP matter to the performance of contemporary multithreaded programs?

Helper locks for fork-join parallel programming

Modeling advanced collective communication algorithms on cell-based systems

Towards scalable and transparent parallelization of multiplayer games using transactional memory support

Exascale computing: the challenges and opportunities in the next decade

GAMBIT: effective unit testing for concurrency libraries

Debugging programs that use atomic blocks and transactional memory

Model-driven autotuning of sparse matrix-vector multiply on GPUs

Scalable communication protocols for dynamic sparse data exchange

Thread to strand binding of parallel network applications in massive multi-threaded systems

Improving parallelism and locality with asynchronous algorithms

A practical concurrent binary search tree

PHANTOM: predicting performance of parallel applications on large-scale parallel machines using a single node

KRASH: reproducible CPU load generation on many cores machines

Is hardware innovation over?

Featherweight X10: a core calculus for async-finish parallelism

NOrec: streamlining STM by abolishing ownership records

Fast tridiagonal solvers on the GPU

The LOFAR correlator: implementation and performance analysis

Scaling LAPACK panel operations using parallel cache assignment

Analyzing lock contention in multithreaded applications

Input-driven dynamic execution prediction of streaming applications

Intra-application shared cache partitioning for multithreaded applications

Compiler aided selective lock assignment for improving the performance of software transactional memory

Scheduling support for transactional memory contention management

CUDAlign: using GPU to accelerate the comparison of megabase genomic sequences

Composable thread coloring

Using data structure knowledge for efficient lock generation and strong atomicity

Symbolic prefetching in transactional distributed shared memory

Leveraging parallel nesting in transactional memory

New abstractions for effective performance analysis of STM programs

Continuous speculative program parallelization in software

Effective communication and computation overlap with hybrid MPI/SMPSs

Supporting lock-free composition of concurrent data objects

SLAW: a scalable locality-aware adaptive work-stealing scheduler for multi-core systems

An optimizing compiler for GPGPU programs with input-data sharing

Applying the concurrent collections programming model to asynchronous parallel dense linear algebra

Application heartbeats for software performance and health

Modeling transactional memory workload performance

The pilot library for novice MPI programmers

Data transformations enabling loop vectorization on multithreaded data parallel architectures

A distributed placement service for graph-structured and tree-structured data

A symbolic verifier for CUDA programs

PHANTOM: predicting performance of parallel applications on large-scale parallel machines using a single node

Content Provider	ACM Digital Library
Author	Zhai, Jidong Zheng, Weimin Chen, Wenguang
Abstract	For designers of large-scale parallel computers, it is greatly desired that performance of parallel applications can be predicted at the design phase. However, this is difficult because the execution time of parallel applications is determined by several factors, including sequential computation time in each process, communication time and their convolution. Despite previous efforts, it remains an open problem to estimate sequential computation time in each process accurately and efficiently for large-scale parallel applications on non-existing target machines. This paper proposes a novel approach to predict the sequential computation time accurately and efficiently. We assume that there is at least one node of the target platform but the whole target system need not be available. We make two main technical contributions. First, we employ deterministic replay techniques to execute any process of a parallel application on a single node at real speed. As a result, we can simply measure the real sequential computation time on a target node for each process one by one. Second, we observe that computation behavior of processes in parallel applications can be clustered into a few groups while processes in each group have similar computation behavior. This observation helps us reduce measurement time significantly because we only need to execute representative parallel processes instead of all of them. We have implemented a performance prediction framework, called PHANTOM, which integrates the above computation-time acquisition approach with a trace-driven network simulator. We validate our approach on several platforms. For ASCI Sweep3D, the error of our approach is less than 5% on 1024 processor cores. Compared to a recent regression-based prediction approach, PHANTOM presents better prediction accuracy across different platforms.
Starting Page	305
Ending Page	314
Page Count	10
File Format	PDF
ISBN	9781605588773
DOI	10.1145/1693453.1693493
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2010-01-09
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Deterministic replay Parallel application Performance prediction Trace-driven simulation
Content Type	Text
Resource Type	Article

Central Library (ISO-9001:2015 Certified)
Indian Institute of Technology Kharagpur
Kharagpur, West Bengal, India | PIN - 721302

See location in the Map
03222 282435
Mail: support@ndl.gov.in

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in