Antony Ian Taylor Rowstron

211 Pages·2016·1.11 MB·English

BULK PRIMITIVES IN LINDA RUN-TIME SYSTEMS
Antony Ian Taylor Rowstron
Submittedfor theDegreeofDoctorofPhilosophy
DepartmentofComputerScience
October1996

Abstract
This thesis investigates techniques for the efficient implementation of the Linda parallel process
coordination modelforopen,distributed computing systems.
The principal focus of the research is on the use of the bulk movement of tuples within open
systems which, contrary to intuition, can result in significant efficiency gains for a large class of
problems. Theemphasisonopensystems—thoseinwhichfuturehistoryofprocesscreationand
deletion cannot beknown atcompile-time —isdueto thecurrent interest inextending theLinda
model to encompass widely distributed computing, as exemplified by the ‘Network Computer’
notion. However,suchopensystemsplacesevereconstraintsonthetypesofoptimisationavailable
relative to closed systems — in particular, the very powerful compile-time analysis techniques
previously usedarenolongerfeasible.
MethodsfortheconstructionofefficientLindakernelsareintroducedbasedonanovelmethod
ofdynamicallyclassifyingtuplespacesaccordingtotheirlocality,whichallowstherun-timemove-
ment of tuple spaces’ locations within the distributed kernel. An important consequence of the
proposed technique is that it does not require any ‘global’ information — it works solely on in-
formation locally available to each component of the distributed kernel. Equally importantly, the
scheme is entirely transparent to the programmer, and therefore requires no user-supplied ‘hints’
or‘pragmas’.
Theimplementedkernelisfullydistributed,consequentlytupleswithinaparticulartuplespace
maybe stored on several physical nodes. The kernel supports standard Linda with multiple tuple
spaces, thecollectprimitive, andanother primitivecalled copy-collect. Thejustification
for the addition of copy-collectis the multiple rd problem which is described in detail in
this dissertation. No acceptable way of overcoming the multiple rd problem without the use of
thecopy-collectprimitivehasbeenpublished.
Theperformanceoftheimplementedkernelisshowntobesignificantlybetterthantheperfor-
manceofakernelthatdoesnotusethebulkmovementoftuples,andthroughusinga“real-world”
example the kernel is shown to provide, under some circumstances, better performance than the
bestcommercially available closedimplementation whichusescompiletimeanalysis.
Finally,anextensionoftheconceptofclassifying tuplespacesispresented, whichgeneralises
the concept leading to a detailed proposal for a multi-layer hierarchical kernel, which is more
scalablethancurrenttraditional implementations.
I
II ABSTRACT
Contents
Abstract I
Dedication XV
Acknowledgements XVII
Declaration XIX
1 Introduction 1
1.1 ThesisOverview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.2 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2 Relatedandbackgroundwork 7
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.2 Linda . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.2.1 TheLindaprimitives . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.2.2 Tuplematching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.2.3 Linda1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.3 Lindaextensions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.3.1 Multipletuplespaces . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.3.2 Newprimitives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.3.3 PiranhaandBauhausLinda . . . . . . . . . . . . . . . . . . . . . . . . 18
2.3.4 TheLindamachine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.3.5 Closingcomments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.4 Theinpandrdpprimitives: theneedforoutordering . . . . . . . . . . . . . 19
2.4.1 Globalsynchronisation . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
2.4.2 outordering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.5 Hostlanguages usedinthisdissertation . . . . . . . . . . . . . . . . . . . . . . 23
2.5.1 ISETL-Lindasyntax . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
2.5.2 C-Lindasyntax . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
2.5.3 Processesandthehostlanguages . . . . . . . . . . . . . . . . . . . . . . 28
III
IV CONTENTS
2.6 Non-determinism andLinda . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
2.6.1 Instructional footprinting . . . . . . . . . . . . . . . . . . . . . . . . . . 30
2.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
3 Themultiplerdproblem 33
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
3.2 Parallelcomposition oftwobinaryrelations . . . . . . . . . . . . . . . . . . . . 34
3.2.1 Formaldefinitionofthecomposition oftwobinaryrelations . . . . . . . 34
3.2.2 Thegeneralapproach toimplementation . . . . . . . . . . . . . . . . . . 34
3.3 Tuplesassemaphores . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
3.3.1 Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
3.4 Streams . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
3.4.1 Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
3.5 Experimentalresults . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
3.5.1 Thebinarysemaphoremethod . . . . . . . . . . . . . . . . . . . . . . . 40
3.5.2 Thestreammethod . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
3.5.3 Experimentalconclusions . . . . . . . . . . . . . . . . . . . . . . . . . 44
3.6 Coarsening theapproach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
3.7 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
4 Copy-collect: AnewprimitivefortheLindamodel 49
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
4.2 Thecopy-collectprimitive . . . . . . . . . . . . . . . . . . . . . . . . . . 49
4.3 Usingcopy-collecttosolvethemultiplerdproblem . . . . . . . . . . . . 55
4.4 Experimentalresults . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
4.5 Modellingtheperformance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
4.6 Comparisonwithothersimilarproposals . . . . . . . . . . . . . . . . . . . . . . 67
4.7 Newcoordination constructs . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
4.8 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
5 Theimplementationofbulkprimitives 79
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
5.2 Reviewofimplementations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
5.2.1 Openimplementations versusclosedimplementations . . . . . . . . . . 80
5.2.2 Closedimplementation techniques . . . . . . . . . . . . . . . . . . . . . 81
5.3 Kernelimplementation techniques . . . . . . . . . . . . . . . . . . . . . . . . . 83
5.3.1 Tupledistribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
5.3.2 Theevalprimitive . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
5.4 Addingexplicitinformation toLindaprograms . . . . . . . . . . . . . . . . . . 89
CONTENTS V
5.5 Implementing multipletuplespaces . . . . . . . . . . . . . . . . . . . . . . . . 91
5.5.1 Linda-Polylith . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92
5.5.2 MTS-Linda . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
5.6 Anaiveapproach toimplementing thebulkprimitives . . . . . . . . . . . . . . . 94
5.7 Classification oftuplespaces . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
5.8 TheYorkKernelII . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
5.9 TupleDistribution withinthekernel . . . . . . . . . . . . . . . . . . . . . . . . 102
5.10 Tuplesandtuplestoragewithinthekernel . . . . . . . . . . . . . . . . . . . . . 102
5.11 TheLocalTupleSpaceManager . . . . . . . . . . . . . . . . . . . . . . . . . . 103
5.12 Implementing theoutprimitive . . . . . . . . . . . . . . . . . . . . . . . . . . 104
5.13 Implementing theinandrdprimitives . . . . . . . . . . . . . . . . . . . . . . 105
5.14 Implementing thebulkprimitives . . . . . . . . . . . . . . . . . . . . . . . . . . 105
5.15 Trackingtuplespacehandles . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106
5.16 Thebulkmovementoftuples . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108
5.17 OptimisingtheYorkKernelII . . . . . . . . . . . . . . . . . . . . . . . . . . . 110
5.17.1 outoptimisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110
5.17.2 LTSMtupleinsertion optimisation . . . . . . . . . . . . . . . . . . . . . 110
5.18 Whyclassify tuplespaces? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
5.19 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116
6 PerformanceoftheYorkKernelII 117
6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117
6.2 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117
6.2.1 Experimental results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119
6.2.2 Experimental conclusions . . . . . . . . . . . . . . . . . . . . . . . . . 124
6.3 TheHoughtransform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131
6.4 Paralleldecomposition oftheHoughtransform. . . . . . . . . . . . . . . . . . . 134
6.4.1 Experimental results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136
6.4.2 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140
6.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141
7 Generalisingtuplespaceclassification 143
7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143
7.2 Eagerness intuplespacemovement . . . . . . . . . . . . . . . . . . . . . . . . 143
7.3 Reclassification ofaRTStoLTSs . . . . . . . . . . . . . . . . . . . . . . . . . 146
7.4 Scalability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147
7.5 N-layerhierarchical kernel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148
7.6 TheTSSnodestructure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150
7.7 Demonstration oftheN-layerhierarchical kernel . . . . . . . . . . . . . . . . . 161
VI CONTENTS
7.8 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 166
8 ConclusionsandFutureresearch 167
8.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167
8.2 Futureresearch . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 168
8.2.1 A“Linda”fordistributed computing? . . . . . . . . . . . . . . . . . . . 169
8.3 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170
8.3.1 Multiplerdoperation . . . . . . . . . . . . . . . . . . . . . . . . . . . 170
8.3.2 Proposalfortheadoption ofthecopy-collectprimitive . . . . . . . 171
8.3.3 AnovelkernelforLinda . . . . . . . . . . . . . . . . . . . . . . . . . . 171
8.3.4 Detaileddescription ofahierarchical kernel . . . . . . . . . . . . . . . . 171
8.4 Closingremarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171
A OverviewoftheLindaimplementations 173
B Sourcecodeforexperimentalresults 175
B.1 Experimentone . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175
B.2 Experimenttwo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 176
B.3 Experimentthree . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177
B.4 Experimentfour . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 178
B.5 Experimentfive . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179
B.6 Experimentsix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 180
List of Figures
2.1 Anexampleofaninteractivesession usingISETL-Linda. . . . . . . . . . . . . . 26
2.2 Howthephilosophers spendtheretimeintheLindaversion. . . . . . . . . . . . 31
2.3 Howthephilosophers mayspendtheirtimeintheoptimisedversion. . . . . . . . 31
3.1 Composition oftwobinaryrelationsrepresented usingthreetuplespaces. . . . . 34
3.2 Execution time for the parallel composition of binary relations when using the
binarysemaphore method. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
3.3 Executionpatterns fortwoandfiveworkerprocesses usingthesemaphore method. 42
3.4 Execution time for the parallel composition of binary relations when using the
streamapproach. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
3.5 Theworkerprocesswhenadoptingacoarserapproachtotheparallelcomposition
ofbinaryrelations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
4.1 Usingthecopy-collectprimitivetosolvethemultiplerdproblem. . . . . . 55
4.2 Executiontimefortheparallelcompositionofbinaryrelationswhenusingthenew
copy-collectprimitive. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
4.3 Execution time for the parallel composition versus the number of pairs in tuple
spaceSwhicheachpairintuplespaceRmatches. . . . . . . . . . . . . . . . . . 59
4.4 Absoluteprimitivecountforthestreammethodforthemultiplerdproblem. . . 61
4.5 Absoluteprimitivecountforthesemaphore methodforthemultiplerdproblem. 61
4.6 Absolute primitive count for the copy-collect method for the multiple rd
problem. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
4.7 Comparison ofabsolute primitivecountsforthestreamandsemaphoremethods. 63
4.8 Comparison ofthenon-concurrent primitivecounts forthestreamandsemaphore
methodstothemultiplerdproblem. . . . . . . . . . . . . . . . . . . . . . . . . 63
4.9 Comparison of the non-concurrent primitive counts for copy-collect and
streammethodstothemultiplerdproblem. . . . . . . . . . . . . . . . . . . . . 64
4.10 Best case expected communication overheads for the three different methods to
themultiplerdproblem. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
4.11 Worst case expected communication overheads for the three different methods to
themultiplerdproblem. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
VII
VIII LISTOFFIGURES
5.1 Intermediate uniform distribution using16kernelprocesses. . . . . . . . . . . . 85
5.2 Anaiveapproach toimplementingthecopy-collectprimitive. . . . . . . . . 95
5.3 TheYorkKernelIIarchitecture. . . . . . . . . . . . . . . . . . . . . . . . . . . 100
5.4 Thetuplestoragedatastructure usedwithinTSSprocesses andtheLTSM. . . . . 103
5.5 Tuplespacehandles embeddedwithintuplespaces. . . . . . . . . . . . . . . . . 107
5.6 Latencyformessagesupto1024bytesinsizeusingPVM. . . . . . . . . . . . . 112
5.7 Latencyformessagesupto100KilobytesinsizeusingPVM. . . . . . . . . . . 112
5.8 Bandwidthversusmessagessizeupto1024bytes. . . . . . . . . . . . . . . . . . 113
5.9 Bandwidthversusmessagessizeupto100Kilobytes. . . . . . . . . . . . . . . . 113
5.10 Thetimetakentosendasinglebyteformessagessizeupto1024bytes. . . . . . 114
5.11 Thetimetakentosendasinglebyteformessagessizeupto100Kilobytes. . . . 114
6.1 Summaryoftheresults ofexperimentone. . . . . . . . . . . . . . . . . . . . . . 119
6.2 Summaryoftheresults ofexperimenttwo. . . . . . . . . . . . . . . . . . . . . . 120
6.3 Summaryoftheresults ofexperimentthree. . . . . . . . . . . . . . . . . . . . . 121
6.4 Summaryoftheresults ofexperimentfour. . . . . . . . . . . . . . . . . . . . . 122
6.5 Summaryoftheresults ofexperimentfive. . . . . . . . . . . . . . . . . . . . . . 123
6.6 Summaryoftheresults ofexperimentsix. . . . . . . . . . . . . . . . . . . . . . 124
6.7 Summaryoftheresults ofallsixexperiments. . . . . . . . . . . . . . . . . . . . 125
6.8 Processing anaeroplane image. . . . . . . . . . . . . . . . . . . . . . . . . . . . 132
6.9 AsimpleimageanditsParameterspaceaftertheHoughtransformhasbeenapplied.133
6.10 Theparalleldecomposition oftheHoughtransform . . . . . . . . . . . . . . . . 135
7.1 Anexamplefivelayerhierarchical kernel. . . . . . . . . . . . . . . . . . . . . . 149
7.2 The pseudo-code for managing an out message within a TSSnode in the N-layer
hierarchical kernel. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151
7.3 The pseudo-code for managing an in (and rd) message within a TSS node in the
N-layerhierarchical kernel. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152
7.4 Thepseudo-code formanaging areplymessage withinaTSSnodeintheN-layer
hierarchical kernel. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154
7.5 Thepseudo-codeformanagingarequestmessagewithinaTSSnodeintheN-layer
hierarchical kernel. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155
7.6 The pseudo-code for managing a packet message within a TSS in the N-layer
hierarchical kernel. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 156
7.7 Thepseudo-codeformanagingacollectmessagewithinaTSSnodeintheN-layer
hierarchical kernel. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158
7.8 The pseudo-code for managing a packet down message within a TSSnode in the
N-layerhierarchical kernel. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 160