Table Of ContentLecture Notes in Computer Science 1511
Editedby G.Goos,J. Hartmanisand J.van Leeuwen
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David R. O’Hallaron (Ed.)
Languages, Compilers,
and Run-Time Systems
for Scalable Computers
4th International Workshop, LCR ’98
Pittsburgh, PA, USA, May 28-30, 1998
Selected Papers
1 3
SeriesEditors
GerhardGoos,KarlsruheUniversity,Germany
JurisHartmanis,CornellUniversity,NY,USA
JanvanLeeuwen,UtrechtUniversity,TheNetherlands
VolumeEditor
DavidR.O’Hallaron
ComputerScienceandElectricalandComputerEngineering
SchoolofComputerScience,CarnegieMellonUniversity
5000ForbesAvenue,Pittsburgh,PA15213-3891,USA
E-mail:[email protected]
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Languages,compilers,andrun-timesystemsforscalable
computers:4thinternationalworkshop;selectedpapers/LCR’98,
Pittsburgh,PA,USA,May28-30,1998.DavidR.O’Hallaron(ed.).-
Berlin;Heidelberg;NewYork;Barcelona;Budapest;HongKong
;London;Milan;Paris;Singapore;Tokyo:Springer,1998
(Lecturenotesincomputerscience;Vol.1511)
ISBN3-540-65172-1
CR SubjectClassification (1991):F.2.2,D.1.3,D.4.4-5,C.2.2, D.3, F.1, C.2.4,
C.3
ISSN0302-9743
ISBN3-540-65172-1Springer-VerlagBerlinHeidelbergNewYork
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Preface
It is a great pleasure to present this collection of papers from LCR ’98, the
Fourth Workshop on Languages, Compilers, and Run-time Systems for Scalable
Computers. The LCR workshop is a bi-annual gathering of computer scientists
who develop software systems for parallel and distributed computers. LCR is
held in alternating years with the ACM Symposium on Principles and Practice
of Parallel Programming (PPoPP) and draws from the same community.
This fourth meeting was held in cooperation with ACM SIGPLAN on the cam-
pus of Carnegie Mellon University, May 28{30, 1998. There were 60 registered
attendees from 9 nations. A total of 47 6-page extended abstracts were sub-
mitted. There were a total of 134 reviews for an average of 2.85 reviews per
submission. Submissions were rank ordered by average review score. The top 23
submissions were selected as full papers and the next 9 as short papers.
The program committee consisted of David Bakken (BBN), Ian Foster (Ar-
gonne),ThomasGross(CMUandETHZurich),CharlesKoelbel(Rice),Piyush
Mehrotra(ICASE),DavidO’Hallaron,Chair(CMU),JoelSaltz(Maryland),Ja-
spal Subhlok (CMU), Boleslaw Szymanski (RPI), Katherine Yelick (Berkeley),
and Hans Zima (Vienna).
Inadditiontothemembersofthecommittee,thefollowingpeoplechairedsessi-
ons: Lawrence Rauchwerger, Peter Brezany, Terry Pratt, Alan Sussman, Sand-
hyaDwarkadisandRajivGupta.Also,thefollowingpeoplegenerouslyprovided
additionalreviews:SigfriedBenkner,ChenDing,GuohaJin,ErwinLaure,Pan-
tona Mario, Eduard Mehofer, and Bernd Wender. We very much appreciate the
e(cid:11)orts of these dedicated volunteers.
BarbaraGrandillodidawonderfuljobofchairingtheorganizingcommitteeand
handling the local arrangements. Thomas Gross and Jaspal Subhlok made nu-
merous suggestions. Peter Dinda, Peter Lieu, Nancy Miller, and Bwolen Yang
also helped out during the workshop itself. We are very grateful for their help.
Finally I would like to thank Mary Lou So(cid:11)a at the Univ. of Pittsburgh, for her
help in getting SIGPLAN support, Bolek Szymanski at RPI, who chaired the
previous meeting and was always helpful and encouraging, and Alfred Hofmann
atSpringer,whoseadviceandencouragementenabledustoproducethisvolume.
Carnegie Mellon University David O’Hallaron
August, 1998 LCR ’98 General/Program Chair
Table of Contents
Expressing Irregular Computations in Modern Fortran Dialects .............1
Jan F. Prins, Siddhartha Chatterjee, and Martin Simons
Memory System Support for Irregular Applications ........................17
John Carter, Wilson Hsieh, Mark Swanson, Lixin Zhang, Erik Brunvand,
Al Davis, Chen-Chi Kuo, Ravindra Kuramkote, Michael Parker,
Lambert Schaelicke, Leigh Stoller, and Terry Tateyama
MENHIR: An Environment for High Performance Matlab ..................27
St(cid:19)ephane Chauveau and Francois Bodin
On the Automatic Parallelization of Sparse and Irregular
Fortran Programs ..........................................................41
Yuan Lin and David Padua
Loop Transformations for Hierarchical Parallelism and Locality ............57
Vivek Sarkar
Dataflow Analysis Driven Dynamic Data Partitioning ......................75
Jodi Tims, Rajiv Gupta, and Mary Lou So(cid:11)a
A Case for Combining Compile-Time and Run-Time Parallelization ........91
Sungdo Moon, Byoungro So, Mary W. Hall, and Brian Murphy
Compiler and Run-Time Support for Adaptive Load Balancing in
Software Distributed Shared Memory Systems ............................107
Sotiris Ioannidis and Sandhya Dwarkadas
E(cid:14)cient Interprocedural Data Placement Optimisation in a
Parallel Library ..........................................................123
Olav Beckmann and Paul H. J. Kelly
A Framework for Specializing Threads in Concurrent Run-Time Systems ..139
Gregory D. Benson and Ronald A. Olsson
Load Balancing with Migrant Lightweight Threads ........................153
David Cronk and Piyush Mehrotra
Integrated Task and Data Parallel Support for Dynamic Applications .....167
James M. Rehg, Kathleen Knobe, Umakishore Ramachandran,
Rishiyur S. Nikhil, and Arun Chauhan
Supporting Self-Adaptivity for SPMD Message-Passing Applications ......181
M. Cermele, M. Colajanni, and S. Tucci
Evaluating the E(cid:11)ectiveness of a Parallelizing Compiler ...................195
Dixie Hisley, Gagan Agrawal and Lori Pollock
VIII Table of Contents
Comparing Reference Counting and Global Mark-and-Sweep
on Parallel Computers ....................................................205
Hirotaka Yamamoto, Kenjiro Taura, and Akinori Yonezawa
Design of the GODIVA Performance Measurement System ................219
Terrence W. Pratt
Instrumentation Database for Performance Analysis of Parallel
Scienti(cid:12)c Applications ....................................................229
Je(cid:11)rey Nesheiwat and Boleslaw K. Szymanski
A Performance Prediction Framework for Data Intensive Applications
on Large Scale Parallel Machines .........................................243
Mustafa Uysal, Tahsin M. Kurc, Alan Sussman, and Joel Saltz
MARS: A Distributed Memory Approach to Shared Memory Compilatio ..259
M.F.P. O’Boyle
More on Scheduling Block-Cyclic Array Redistribution ....................275
Fr(cid:19)ed(cid:19)eric Desprez, St(cid:19)ephane Domas, Jack Dongarra, Antoine Petitet,
Cyril Randriamaro, and Yves Robert
Flexible and Optimized IDL Compilation for Distributed Applications ....288
Eric Eide, Jay Lepreau, and James L. Simister
QoS Aspect Languages and Their Runtime Integration ....................303
Joseph P. Loyall, David E. Bakken, Richard E. Schantz, John A. Zinky,
David A. Karr, Rodrigo Vanegas, and Kenneth R. Anderson
Statistical Properties of Host Load ........................................319
Peter A. Dinda
Locality Enhancement for Large-Scale Shared-Memory Multiprocessors ...335
Tarik Abdelrahman, Naraig Manjikian, Gary Liu, and S. Tandri
Language and Compiler Support for Out-of-Core Irregular Applications
on Distributed-Memory Multiprocessors ..................................343
Peter Brezany, Alok Choudhary, and Minh Dang
Detection of Races and Control-Flow Nondeterminism ....................351
Mindong Feng and Chung Kwong Yuen
Improving Locality in Out-of-Core Computations Using Data
Layout Transformations ..................................................359
M. Kandemir, A. Choudhary, and J. Ramanujam
Optimizing Computational and Spatial Overheads in Complex
Transformed Loops .......................................................367
Dattatraya Kulkarni and Michael Stumm
Table of Contents IX
Building a Conservative Parallel Simulation with Existing
Component Libraries .....................................................378
Chu-Cheow Lim and Yoke-Hean Low
A Coordination Layer for Exploiting Task Parallelism with HPF ..........386
Salvatore Orlando and Ra(cid:11)aele Perego
InterAct: Virtual Sharing for Interactive Client-Server Applications .......394
Srinivasan Parthasarathy and Sandhya Dwarkadas
Standard Templates Adaptive Parallel Library (STAPL) ..................402
Lawrence Rauchwerger, Francisco Arzu, and Koji Ouchi
Author Index .............................................................411
Expressing Irregular Computations
?
in Modern Fortran Dialects
JanF.Prins,SiddharthaChatterjee,andMartinSimons
DepartmentofComputerScience
TheUniversityofNorthCarolina
ChapelHill,NC27599-3175
fprins,sc,[email protected]
Abstract. ModerndialectsofFortranenjoywideuseandgoodsupportonhigh-
performancecomputersasperformance-orientedprogramminglanguages.Bypro-
vidingtheabilitytoexpressnesteddataparallelisminFortran,weenableirregular
computationstobeincorporatedintoexistingapplicationswithminimalrewriting
and without sacrificing performance within the regular portions of the applica-
tion.Sinceperformanceofnesteddata-parallelcomputationisunpredictableand
oftenpoorusingcurrentcompilers,weinvestigatesource-to-sourcetransforma-
tiontechniquesthatyieldFortran90programswithimprovedperformanceand
performancestability.
1 Introduction
Modernscienceandengineeringdisciplinesmakeextensiveuseofcomputersimulati-
ons.As these simulations increase in size and detail, the computational costs of naive
algorithms can overwhelm even the largest parallel computers available today. Fortu-
nately,computationalcostscanbereducedusingsophisticatedmodelingmethodsthat
varymodelresolutionasneeded,coupledwithsparseandadaptivesolutiontechniques
thatvarycomputationaleffortintimeandspaceasneeded.Suchtechniqueshavebeen
developedandareroutinelyemployedinsequentialcomputation,forexample,incosmo-
logicalsimulations(usingadaptiven-bodymethods)andcomputationalfluiddynamics
(usingadaptivemeshingandsparselinearsystemsolvers).
However, these so-called irregular or unstructured computations are problematic
for parallel computation, where high performance requires equal distribution of work
over processors and locality of reference within each processor. For many irregular
computations, the distribution of work and data cannot be characterized a priori, as
thesequantitiesareinput-dependentand/orevolvewiththecomputationitself.Further,
irregular computations are difficult to express using performance-oriented languages
such as Fortran, because there is an apparent mismatch between data types such as
trees, graphs, and nested sequences characteristic of irregular computations and the
staticallyanalyzablerectangularmulti-dimensionalarraysthatarethecoredatatypesin
?ThisresearchwassupportedinpartbyNSFGrants#CCR-9711438and#INT-9726317.Chat-
terjeeissupportedinpartbyNSFCAREERAward#CCR-9501979.Simonsissupportedbya
researchscholarshipfromtheGermanResearchFoundation(DFG).
D.O’Hallaron(Ed.):LCR’98,LNCS1511,pp.1–16,1998.
(cid:13)c Springer-VerlagBerlinHeidelberg1998
2 J.F.Prins,S.Chatterjee,andM.Simons
modernFortrandialectssuchasFortran90/95[19],andHighPerformanceFortran(HPF)
[16].Irregulardatatypescanbeintroducedusingthedataabstractionfacilities,witha
representationexploitingpointers.Optimizationofoperationsonsuchanabstractdata
typeiscurrentlybeyondcompile-timeanalysis,andcompilershavedifficultygenerating
high-performanceparallelcodeforsuchprograms.Thispaperprimarilyaddressesthe
expressionofirregularcomputationsinFortran95,butdoessowithaparticularview
of the compilation and high performance execution of such computations on parallel
processors.
ThemodernFortrandialectsenjoyincreasinguseandgoodsupportasmainstream
performance-orientedprogramminglanguages.Byprovidingtheabilitytoexpressirre-
gularcomputationsasFortranmodules,andbypreprocessingthesemodulesintoaform
thatcurrentFortrancompilerscansuccessfullyoptimize,weenableirregularcomputa-
tionstobeincorporatedintoexistingapplicationswithminimalrewritingandwithout
sacrificingperformancewithintheregularportionsoftheapplication.
Forexample,considertheNASCG(ConjugateGradient)benchmark,whichsolves
anunstructuredsparselinearsystemusingthemethodofconjugategradients[2].Within
thedistributedsamplesequentialFortransolution,79%ofthelinesofcodearestandard
Fortran77concernedwithproblemconstructionandperformancereporting.Thenext
16%consistofscalarandregularvectorcomputationsoftheBLAS2variety[17],while
thefinal5%ofthecodeistheirregularcomputationofthesparsematrix-vectorproduct.
Clearlywewanttorewriteonlythis5%ofthecode(whichperforms97%ofthework
in the class B computation), while the remainder should be left intact for the Fortran
compiler. This is not just for convenience. It is also critical for performance reasons;
followingAmdahl’sLaw,astheperformanceoftheirregularcomputationimproves,the
performanceoftheregularcomponentbecomesincreasinglycriticalforsustainedhigh
performance overall. Fortran compilers provide good compiler/annotation techniques
toachievehighperformancefortheregularcomputationsintheproblem,andcanthus
provideanefficientandseamlessinterfacebetweentheregularandirregularportionsof
thecomputation.
We manually applied the implementation techniques described in Sect. 4 to the
irregularcomputationintheNASCGproblem.TheresultantFortranprogramachieved
a performance on the class B NAS CG 1.0 benchmark of 13.5 GFLOPS using a 32
processorNECSX-4[25].Webelievethistobethehighestperformanceachievedfor
thisbenchmarktodate.Itexceeds,byafactorof2.6,thehighestperformancereportedin
thelastNPB1.0report[27],andisslightlyfasterthanthe12.9GFLOPSrecentlyachieved
usinga1024processorCrayT3E-900[18].Theseencouraginginitialresultssupportthe
thesisthathigh-levelexpressionandhigh-performanceforirregularcomputationscan
besupportedsimultaneouslyinaproductionFortranprogrammingenvironment.
2 Expressing Irregular Computations Using Nested Data
Parallelism
Weadoptthedata-parallelprogrammingmodelofFortranasourstartingpoint.Thedata-
parallelprogrammingmodelhasproventobepopularbecauseofitspowerandsimplicity.
Data-parallellanguagesarefoundedontheconceptofcollections(suchasarrays)and
