Scalable Task Parallelism for NUMA: A Uniform Abstraction for Coordinated Scheduling and ...

14 Pages·2017·2.18 MB·English

Scalable Task Parallelism for NUMA: A Uniform Abstraction
for Coordinated Scheduling and Memory Management
Andi Drebes Antoniu Pop Karine Heydemann
TheUniversityofManchester TheUniversityofManchester SorbonneUniversités
SchoolofComputerScience SchoolofComputerScience UPMCUnivParis06
OxfordRoad OxfordRoad CNRS,UMR7606,LIP6
ManchesterM139PL ManchesterM139PL 4,PlaceJussieu
UnitedKingdom UnitedKingdom F-75252ParisCedex05,France
[email protected] [email protected] [email protected]
Albert Cohen Nathalie Drach
INRIAandÉcoleNormale SorbonneUniversités
Supérieure UPMCUnivParis06
45rued’Ulm CNRS,UMR7606,LIP6
F-75005Paris 4,PlaceJussieu
France F-75252ParisCedex05,France
[email protected] [email protected]
ABSTRACT Keywords
Dynamic task-parallel programming models are popular on Task-parallel programming; NUMA; Scheduling; Memory
shared-memorysystems,promisingenhancedscalability,load allocation; Data-flow programming.
balancing and locality. Yet these promises are undermined
bynon-uniformmemoryaccess(NUMA).Weshowthatus-
ing NUMA-aware task and data placement, it is possible
to preserve the uniform abstraction of both computing and 1. INTRODUCTION
memoryresourcesfortask-parallelprogrammingmodelswhile High-performance systems are composed of hundreds of
achieving high data locality. Our data placement scheme general-purposecomputingunitsanddozensofmemorycon-
guarantees that all accesses to task output data target the trollers to satisfy the ever-increasing need for computing
local memory of the accessing core. The complementary power and memory bandwidth. Shared memory program-
task placement heuristic improves the locality of task input ming models with fine-grained concurrency have success-
data on a best effort basis. Our algorithms take advantage fullyharnessedthecomputationalresourcesofsucharchitec-
of data-flow style task parallelism, where the privatization tures [3, 33, 28, 30, 31, 10, 19, 8, 9, 7, 35]. In these models,
oftaskdataenhancesscalabilitybyeliminatingfalsedepen- the programmer exposes parallelism through the creation
dencesandenablingfine-graineddynamiccontroloverdata of fine-grained units of work, called tasks, and the specifi-
placement. Thealgorithmsarefullyautomatic,application- cation of synchronization that constrains the order of their
independent,performance-portableacrossNUMAmachines, execution. Arun-timesystemmanagestheexecutionofthe
andadapttodynamicchanges. Placementdecisionsusein- task-parallelapplicationandactsasanabstractionlayerbe-
formation about inter-task data dependences readily avail- tween the program and the underlying hardware and soft-
ableintherun-timesystemandplacementinformationfrom ware environment. That is, the run-time is responsible for
the operating system. We achieve 94% of local memory ac- bookkeeping activities necessary for the correctness of the
cessesona192-coresystemwith24NUMAnodes,upto5× execution (e.g., the creation and destruction of tasks and
higher performance than NUMA-aware hierarchical work- their synchronization), interfacing with the operating sys-
stealing, and even 5.6× compared to static interleaved allo- tem for resource management (e.g., allocation of data and
cation. Finally,weshowthatstate-of-the-artdynamicpage meta-data for tasks, scheduling tasks to cores) and efficient
migrationbytheoperatingsystemcannotcatchupwithfre- exploitation of the hardware.
quentaffinitychangesbetweencoresanddataandthusfails This concept relieves the programmer from dealing with
to accelerate task-parallel applications. detailsofthetargetplatformandthusgreatlyimprovespro-
Permissiontomakedigitalorhardcopiesofallorpartofthisworkforpersonalor ductivity. Yet it leaves issues related to efficient interaction
classroomuseisgrantedwithoutfeeprovidedthatcopiesarenotmadeordistributed withsystemsoftware,efficientexploitationofthehardware,
forprofitorcommercialadvantageandthatcopiesbearthisnoticeandthefullcita-
and performance portability to the run-time. On today’s
tiononthefirstpage. Copyrightsforcomponentsofthisworkownedbyothersthan
ACMmustbehonored.Abstractingwithcreditispermitted.Tocopyotherwise,orre- systems with non-uniform memory access (NUMA), mem-
publish,topostonserversortoredistributetolists,requirespriorspecificpermission ory latency depends on the distance between the request-
and/[email protected].
ing cores and the targeted memory controllers. Efficient
PACT’16,September11-15,2016,Haifa,Israel
resource usage through task scheduling needs to go handin
(cid:13)c 2016ACM.ISBN978-1-4503-4121-9/16/09...$15.00 handwiththeoptimizationofmemoryaccessesthroughthe
DOI:http://dx.doi.org/10.1145/2967938.2967946 placementofphysicalpages. Thatis,memoryaccessesmust
be kept local in order to reduce latency and data must be across NUMA machines and transparently adapts to dy-
distributed across memory controllers to avoid contention. namicchangesatruntime. Thedetailedinformationabout
The alternative of abstracting computing resources only theaffinitiesbetweentasksanddatarequiredbythesetech-
and leaving NUMA-specific optimization to the application niques is either readily available or can be obtained au-
is far less attractive. The programmer would have to take tomatically in the run-times of task-parallel programming
into account the different characteristics of all target sys- models with inter-task dependences, such as StarSs [30],
tems (e.g., the number of NUMA nodes, their associated OpenMP 4 [28], SWAN [33] and OpenStream [31], which
amountofmemoryandaccesslatencies),topartitionappli- allow the programmer to make inter-task datadependences
cation data properly and to place the data explicitly us- explicit. While specifying the precise task-level data-flow
ing operating system-specific interfaces. For applications ratherthansynchronizationconstraintsalonerequiresmore
with dynamic, data-dependent behavior, the programmer initial work for programmers, this effort is more than off-
would also have to provide mechanisms that constantly re- set by the resulting benefits in terms of performance and
acttochangesthroughouttheexecutionasaninitialplace- performance portability.
ment with high data locality at the beginning might have The paper is organized as follows. Section 2 presents the
to be revised later on. Such changes would have to be co- principles of enhanced work-pushing. For a more complete
ordinated with the run-time system to prevent destructive discussion of our solutions, we propose multiple heuristics
performanceinterference,introducingatightandundesired taking into account the placement of input data, output
coupling between the run-time and the application. data or both. Section 3 introduces deferred allocation, in-
Ontheoperatingsystemside,optimizationsarecompelled cludingabriefoutlineofthetechnicalsolutionsemployedfor
to place tasks and data conservatively [13, 24], unless pro- fine-grained data placement. Sections 4 and 5 present the
videdwithdetailedaffinityinformationbytheapplication[5, experimental methodology and results. A comparison with
6],high-levellibraries[26]ordomainspecificlanguages[20]. dynamicpagemigrationispresentedinSection6. Section7
Furthermore,astask-parallelrun-timesoperateinuser-space, discusses the most closely related work, before we conclude
aseparatekernelcomponentwouldaddadditionalcomplex- in Section 8.
itytothesolution;thisadvocatesforauser-spaceapproach.
This paper shows that it is possible to efficiently and por- 2. TASKSCHEDULINGWITHENHANCED
tably exploit dynamic task parallelism on NUMA machines
WORK-PUSHING
withoutexposingprogrammerstothecomplexityofthesesys-
tems, preserving a simple, uniform abstract view for both Let us start with terminology and hypotheses about the
memoryandcomputations,yetachievinghighlocalityofmem- programming and execution models.
ory accesses. Our solution exploits the task-parallel data-
flow programming style and its transparent privatization of 2.1 Anabstractmodelfortaskparallelism
task data. This allows the run-time to determine a task’s Our solutions are based on shared memory task-parallel
working set, enabling transparent, fine-grained control over programming models with data dependences. Each task is
task and data placement. associated with a set of incoming data dependences and a
Basedonthepropertiesoftask-privatedata,weproposea setofoutgoing data dependences,asillustratedbyFigure1.
dynamic task and data placement algorithm to ensure that Each dependence is associated with a contiguous region of
inputandoutputdataarelocalandthatinteractsconstruc- memory, called input buffer and output buffer for incoming
tively with work-stealing to provide load-balancing across and outgoing dependences, respectively. The addresses of
both cores and memory controllers: these buffers are collected in the task’s frame, akin to the
activation frame storing a function’s arguments and local
• Ourmemoryallocationmechanism,calleddeferred al-
variables in the call stack. While the frame is unique and
location,avoidsmakingearlyplacementdecisionsthat
allocated at the task’s creation time, its input and output
couldlaterharmperformance. Inparticular,themem-
buffers may be placed on different NUMA nodes and allo-
ory to store task output data is not allocated until
cated later in the life cycle of the task, but no later than
the task placement is known. The mechanism hands
the beginning of the execution of the task, reading from
over this responsibility to the producer task on its lo-
input buffers and writing into output buffers. Buffer place-
cal NUMA node. This scheme guarantees that all ac-
mentandallocationtimehaveadirectinfluenceonlocality
cesses to task output data are local. Control over data
and task-data affinity. Since we ought to offer a uniform
placementisobtainedthroughtheprivatizationoftask
abstraction of NUMA resources, we assume input and out-
output data.
putbuffersaremanagedbytherun-timesystemratherthan
• To enhance the locality of read memory accesses, we explicitly allocated by the application. This is the case of
buildonearlierwork[14]andproposeenhanced work- programmingmodelssuchasOpenStream[31]andCnC[8],
pushing,awork-sharingmechanismthatinteractscon- but not of StarSs [30] and OpenMP 4.0; see Section 7 for
structively with deferred allocation. Since the inputs further discussion. We say that a task t depends on an-
c
of a task are outputs of another task, the location of othertaskt ift hasanoutgoingdependenceassociatedto
p p
input data is determined by deferred allocation when abufferbandift hasanincomingdependenceassociatedto
c
the producer tasks execute. Enhanced work-pushing b. In this scenario the task t is referred to as the producer
p
is a best-effort mechanism that places a task accord- and t is the consumer.
c
ing to these locations before task execution and thus Although this is not a fundamental requirement in our
before allocating memory for the task’s outputs. work, we will assume for simplicity that a task executes
Thiscombinationofenhancedwork-pushing anddeferredal- from beginning to end without interruption. A task be-
locationisfullyautomatic,application-independent,portable comes ready for execution when all of its dependences have
buffers. As a consequence, the task’s input data is entirely
locatedonasinglenode. Thispropertyisusedbythework-
pushing technique, in which the worker activating a task
only becomes its owner if the task’s input data is stored on
the worker’s node. If the input data is located on another
node,thetaskistransferredtoarandomworkerassociated
toacoreonthatnode. Theapproachremainslimitedhow-
ever: (1)asitassumesthatallinputsarelocatedonthesame
nodeitisill-suitedforinputdatalocatedonmultiplenodes,
Figure 1: Most general case for a task t: n inputs of size
and (2) it does not optimize for outgoing dependences.
δ0,...,δn−1,moutputsofsizeδ0,...,δm−1,dataplacedon
i i o o Below,wefirstpresentenhanced work-pushing,ageneral-
n+m NUMA nodes N0,...,Nn−1,N0,...,Nm−1.
i i o o ization of work-pushing, capable of dealing with input data
distributedovermultipleinputbufferspotentiallyplacedon
been satisfied, i.e., when its producers of input data have
differentnodes. Thistechniqueservesasabasisforthecom-
completed and when its consumers have been created with
plementary deferred allocation technique, presented in the
theaddressesoftheirframescommunicatedtothetask. The
nextsection,thatallowstherun-timetoimprovetheplace-
working set of a task is defined as the union of all memory
ment of output buffers. We introduce three work-pushing
addresses that are accessed during its execution. Note that
heuristics that schedule a task according to the placement
the working set does not have to be identical to the union
of its input or output data or both. This complements the
ofatask’sinputandoutputbuffers(e.g.,ataskmayaccess
study of the effects of work-pushing on data locality. And
globally shared data structures). However, since our algo-
experimentally in Section 5, it enables us to show the limi-
rithmsrequireaccuratecommunicationvolumeinformation,
tationsofNUMA-awareschedulingalone,limitedtopassive
we assume that the bulk of the working set of each task is
reactions to a given data placement.
constituted by its input and output data. This is the case
for all of the benchmarks studied in the experimental eval-
2.3 Enhancedwork-pushing
uation.
A worker thread is responsible for the execution of tasks The names of the three heuristics for enhanced work-
onitsassociatedcore. Eachworkerisprovidedwithaqueue pushing are input-only, output-only and weighted. The first
oftasksreadyforexecutionfromwhichitpopsandexecutes two heuristics take into account incoming and outgoing de-
tasks. When the queue is empty, the worker may obtain a pendences only, respectively. The weighted heuristic takes
task from another one through work-stealing [4]. A task intoaccountalldependences,butassociatesdifferentweights
is pushed to the queue by the worker that satisfies its last toincomingandoutgoingdependencestohonorthefactthat
remaining dependence. We say that this worker activates readandwriteaccessesusuallydonothavethesamelatency.
the task and becomes its owner. Algorithm 1 shows how the heuristics above are used by
The execution of a task-parallel program starts with a thefunctionactivate,whichiscalledwhenaworkerw acti-
root task derived from the main function of a sequential vates a task t (i.e., when the task becomes ready). Lines 1
process. Newtasksarecreateddynamically. Thepartofthe to 3 define variables uin and uout, indicating which types
program involved in creating new tasks is called the control of dependences should be taken into account according to
program. Ifonlytheroottaskcreatesothertaskswespeakof the heuristic h. The variables used to determine whether
a sequential control program, otherwise of a parallel control thenewlyactivatedtaskneedstobetransferredtoaremote
program. node are initialized in lines 5 to 8: the data array stores
the cumulated size of input and output buffers of t for each
2.2 WeaknessesoftaskparallelismonNUMA
of the N nodes of the system, D stands for the incoming
in
systems
dependences of t and D for its outgoing dependences.
out
Whether memory accesses during the execution of a task The for loop starting at Line 10 iterates over a list of
target the local memory controller of the executing core or triples with a set of dependences Dcur, a variable ucur indi-
some remote memory controller depends on the placement cating whether the set should be taken into account, and a
oftheinputandoutputbuffersandontheworkerexecuting weight wcur associated to each type of dependence. During
thetask. Theseaffinitiesarehighlydynamicandcandepend thefirstiteration,Dcur isidenticalwithDin andduringthe
on many factors, such as: second iteration identical with Dout. For each dependence
in D , a second loop in Line 12 determines the buffer b
cur
• the order of task creations by the control program; used by the dependence, the size s of the buffer as well as
b
• the execution order of producers; the node nb containing the buffer. The node on which a
buffer is placed might be unknown if the buffer has not yet
• the duration of each task;
been placed by the operating system, e.g., using the first-
• work-stealing events; touchscheme and the buffer has been allocated but not yet
written. Ifthisisthecase,itssizeisaddedtothetotalsize
• orresourceavailability(e.g.,availablememorypernode).
s , but not included into the per-node statistics. Other-
tot
In earlier work [14], we showed that some of these issues wise, the data array is updated accordingly by multiplying
can be mitigated by using work-pushing. Similar to the ab- s with the weight w .
b cur
stract model discussed above, the approach assumes that Oncethetotalsizeandtheweightednumberofbytesper
tasks communicate through task-private buffers. However, node have been determined, the procedure checks whether
it also assumes that all input data of a task is stored in a the task should be pushed to a remote node. Tasks whose
single,contiguousmemoryregionratherthanmultipleinput overall size of dependences is below a threshold are added
Algorithm 1: activate(w, t) higher amount of tasks than others. Work-stealing redis-
tributes tasks among the remaining workers and thus pre-
1 if h=input only then (uin,uout)←(true,false) vents the system from load imbalance, but cannot improve
2 else if h=output only then (uin,uout)←(false,true) overalldatalocalityiftheinitialdatadistributionwaspoor.
3 else if h=weighted then (uin,uout)←(true,true) Classicaltaskparallelrun-timesallocatebuffersduringtask
4
creation[8,33,31];hencedatadistributionmainlydepends
5 data[0,...N −1]←(cid:104)0,...,0(cid:105)
onthecontrolprogram. Asequentialcontrolprogramleads
6 Din ←in deps(t)
to poorly placed data, while a parallel control program lets
7 Dout ←out deps(t)
8 stot ←0 work-stealing evenly distribute task creation and buffer al-
location. However, writing a parallel control program is al-
9
10 for (Dcur,ucur,wcur) in readychallenginginitself,evenforprogramswithregularly-
(cid:104)(D ,u ,w ), (D ,u ,w )(cid:105) do structured task graphs. Additionally ensuring an equal dis-
in in in out out out
11 if ucur =true then tribution of data across NUMA nodes through the control
12 for d∈Dcur do programisevenmorechallengingorinfeasible,especiallyfor
13 sb ←size of(buffer of(d)) applicationswithlessregularly-structuredtaskgraphs(e.g.,
14 nb ←node of(buffer of(d)) if the structure of the graph depends on input data). Such
15 stot ←stot+sb optimizations also reject efficient exploitation of NUMA to
16 if nb (cid:54)=unknown then the programmer and are thus contrary to the idea of ab-
17 data[nb]←data[nb]+wcur·sb
18 end straction from the hardware by the run-time.
19 end Inthefollowingsection,weintroduceaNUMA-awareallo-
20 end catorthatcomplementstheinputonly work-pushingheuris-
21 end tic and that decouples data locality from the control pro-
22 gram, leaving efficient exploitation to the run-time.
23 if stot <threshold then
24 add to local queue(w, t) 3. DEFERREDALLOCATION
25 else
26 nmin ←node with min access cost(data) NUMA-awareallocationcontrolstheplacementofdataon
27 specificnodes. Ourproposedschemetomakethesedecisions
28 if nmin (cid:54)=local node of worker(w) then transparent relies on per-node memory pools to control the
29 wdst ←random worker on node(nmin) placement of task buffers.
30 res ←transfer task(t,wdst)
31 if res=failure then 3.1 Per-nodememorypools
32 add to local queue(w, t)
33 end Per-nodememorypoolscombineamechanismforefficient
34 else reuse of blocks of memory with the ability to determine on
35 add to local queue(w, t) which nodes blocks are placed. Each NUMA node has a
36 end memory pool that is composed of k free lists L0 to Lk−1,
37 end where Li contains blocks of size 2Smin+i bytes. When a
worker allocates a block of size s, it determines the corre-
to the local queue (Line 24) to avoid cases in which the spondinglistLj with2Smin+j−1 <s≤2Smin+j andremoves
the first block of that list. If the list is empty, it allocates a
overhead of a remote push cannot be compensated by the
largerchunkofmemoryfromtheoperatingsystem,removes
improvement on task execution time. For tasks with larger
thefirstblockfromthechunkandaddstheremainingparts
dependences, the run-time determines the node n with
min to the free list.
the minimal overall access cost (Line 29). The access cost
Acommonallocationstrategyofoperatingsystemsisfirst-
for a node N is estimated by summing up the access costs
i touch allocation, composed of two steps. The first step re-
toeachnodeN containingatleastoneofthebuffers,which
j ferredtoaslogical allocation istriggeredbythesystemcall
inturncanbeestimatedbymultiplyingtheaveragelatency
betweenN andN .1 Ifn isdifferentfromthelocalnode used by the application to request additional memory and
i j min only extends the application’s address space. The actual
n of the activating worker, the run-time tries to transfer
lcl physical allocation is triggered upon the first write to the
t to a random worker on n . If this fails, e.g., if the data
min memory region and places the corresponding page on the
structure of the targeted worker receiving remotely pushed
same node as the writing core. Hence, a block that origi-
tasks is full, the task is added to the local queue (Line 32).
natesfromanewlyallocatedchunkisnotnecessarilyplaced
2.4 Limitationsofenhancedwork-pushing on any node.
However, when a block is freed, it has been written by a
The major limitation of enhanced work-pushing is that,
producer and it is thus safe to assume that the block has
regardless of the heuristic, it can only react passively to
been placed through physical allocation. The identifier of
a given data placement. This implies that data must al-
the containing node can be obtained through a system call,
ready be well-distributed across memory controllers if all
which enables the run-time to return the block to the cor-
tasksshouldtakeadvantageofthisschedulingstrategy. For
rect memory pool. To avoid the overhead of a system call
poorly distributed data, e.g., if all data is placed on a sin-
each time a block is freed, information on the NUMA node
gle node, a subset of the workers receives a significantly
containing a block is cached in a small meta-data section
1We estimated the latencies based on the distance between associated to the block. This memory pooling mechanism
each pair of nodes reported by the numactl tool provided providesthreefundamentalpropertiesfordeferredallocation
by LibNUMA [21]. presented below. First, it ensures that allocating a block
from a memory pool always returns a block that has not concerns that led to the delegation of task management to
beenplacedyetorablockthatisknowntobeplacedonthe the run-time.
node associated to the memory pool. Second, data can be Thanks to deferred allocation, buffers allocated for early
placed on a specific node with very low overhead. Finally, taskscanbereusedatalaterstage. Thedifferenceisshown
the granularity for data placement is decoupled from the in Figures 3a and 3c. In the first case, all three buffers b ,
i
usual page granularity as a block may be sized arbitrarily. b andb areallocatedbeforethedependenttaskst to
i+1 i+2 i
t are executed. In the latter case, the buffer used by t
i+3 i
and t can be reused as the input buffer of t . Parallel
i+1 i+3
3.2 Principlesofdeferredallocation controlprogramsalsobenefitfromdeferredallocationasthe
minimal number of buffers along a path of dependent tasks
The key idea of deferred allocation is to delay the alloca-
canbedecreasedbyone(e.g.,inFigure3bonlyb andb
tion and thus the placement of each task buffer until the i i+1
are simultaneously live and b can be reused for b when
node executing the producer that writes to the buffer is i i+2
using deferred allocation).
known. This guarantees that accesses to output buffers are
always local. The classical approach in run-times for de-
3.3 Compatibilitywithwork-pushing
pendent tasks is to allocate input buffers upon the creation
of a task [8, 33, 31] or earlier [30]. Instead, we propose to Deferredallocationguaranteeslocalwriteaccesses,butit
let each input buffer for a consumer task tc to be allocated does not influence the locality of read accesses. By com-
by the producer task tp writing into it, immediately before bining deferred allocation with the input-only heuristic of
task tc starts execution. Since the input buffer of tc is an enhanced work-pushing, it is possible to optimize for both
output buffer of tp, the location of input data in tc is effec- read and write accesses.
tively determined by its producer(s). In the following, we Itisimportanttonotethatneithertheoutput-onlyheuris-
usethetermimmediateallocation todistinguishthedefault tic nor the weighted heuristic can be used since the output
allocationschemeinwhichinputbuffersareallocatedupon buffers of a task are not determined upon task activation
creation from deferred allocation. when the work-pushing decision is taken.
Figure 2a shows the implications of immediate allocation
on data locality for a task t. All input buffers of t are al-
located on the node Nc on which the creator of t operates. 4. EXPERIMENTALSETUP
The same scheme applies to the creators t0 to tm−1, caus-
c,o c,o Fortheexperimentalevaluationweimplementedenhanced
ing the input buffersof the tasks t0 totm−1 tobe allocated
o o work-pushinganddeferredallocationintherun-timesystem
on nodes N0 to Nm−1, respectively. In the worst case for
o o of the OpenStream project [29]. We start with an overview
data locality, t is stolen by a worker operating on neither
of the software and hardware environment used in our ex-
N nor N0 to Nm−1 and all memory accesses of t target
c o o periments,followedbyapresentationoftheselectedbench-
memory on remote nodes.
marks.
Whenusingdeferredallocation, theinputbuffersoftare
not allocated before its producers start execution and the
4.1 Softwareenvironment
output buffers of t are not allocated before t is activated
(Figure 2b). When t becomes ready, all of its input buffers OpenStream [31] is a task-parallel, data-flow program-
have received input data from the producers of t and have ming model implemented as an extension to OpenMP. Ar-
been placed on up to n different nodes N0 to Nn−1 (Fig- bitrary dependence patterns can be used to exploit task,
i i
ure 2c). The data locality impact of deferred allocation pipeline and data parallelism. Each data-flow dependence
is illustrated in Figure 2d, showing the placement at the issemanticallyequivalenttoacommunicationandsynchro-
moment when the worker executing t has been determined. nizationeventwithinanunboundedFIFOqueuereferredto
Regardless of any possible placement of t, all of its output as a stream. Pragmatically, this is implemented by compil-
buffersareplacedonthesamenodeastheworkerexecuting ingdependencesasaccessestotaskbuffersdynamicallyallo-
thetask. Hence,usingdeferredallocation,writeaccessesare catedatexecutiontime: writestostreamsresultinwritesto
guaranteedtotargetlocalmemory. Furthermore,thisprop- the buffers of the tasks consuming the data, while read ac-
ertyisindependentfromtheplacementofthecreatingtasks cessestostreamsbyconsumer tasks aretranslatedtoreads
t and t0 to tm−1, which effectively decouples data local- from their own, task-private buffers.
c c,o c,o
ity from the control program. Even for a sequential control We implemented the optimizations presented in this pa-
program, data is distributed over the different nodes of the perintothepubliclyavailablerun-timeofOpenStream[29].
machineaccordingtowork-stealing. Thisway,work-stealing Crucially,werelyonthefactthatOpenStreamprogramsare
does not only take the role of a mechanism responsible for written with programmer annotations explicitly describing
computationalloadbalancing,butalsotheroleasamecha- theflowofdatabetweentasks. Thisprecisedata-flowinfor-
nism for load balancing across memory controllers. mation is preserved during compilation and made available
Animportantsideeffectofdeferredallocationisasignif- to the run-time library. We leverage this essential seman-
icant reduction of the memory footprint. With a sequential tic information to determine, at run-time and before task
controlprogram,alltasksarecreatedbyasingle“root”task. execution, how much data is exchanged by any given task.
This causes a large number of input buffers to be allocated OpenStreamprogramsaredynamicallyload-balanced,wor-
early on, while the actual working set of live buffers might ker threads use hierarchical work-stealing to acquire and
be much smaller. A parallel control program can mitigate execute tasks whose dependences have been satisfied. If
the effects of early allocation, e.g., by manually throttling work-pushing is enabled, workers can also receive tasks in
task creation as shown inFigure 3b. However, this requires adedicatedmulti-producersingle-consumerqueue[14]. Our
significant programmer effort and hurts the separation of experiments use one worker thread per core.
(a)Immediateallocationwithoutbufferreuse
create create
(a)Immediateallocation (b)Deferredallocation: aftercreationoft
(b)Throttlingoftaskcreations
reuse
(c)Deferredallocationfavoringbufferreuse
(c)Deferredallocation: activationoft (d)Deferredallocation: finalplacement Figure 3: Allocation and reuse
Figure 2: Allocation schemes
4.2 Hardwareenvironment usingLibNUMA,initializesitandmeasuresexecutiontime
The experiments were conducted on two many-core sys- forasequenceofmemoryaccessestothisbufferfromacore
tems. onaspecificnode. Eachsequencetraversesthewholebuffer
Opteron-64isaquad-socketsystemwithfourAMDOp- from beginning to end in steps of 64 bytes, such that each
teron6282SEprocessorsrunningat2.6GHz,usingScientific cache line is only accessed once. The buffer size was set to
Linux6.2withkernel3.10.1. Themachineiscomposedof4 1GiB to ensure data is evicted from the cache before it is
physicalpackages,with2diesperpackage,eachdiecontain- reused and thus to measure only memory accesses that are
ing8coresorganizedinpairs. Eachpairsharesthefirst-level satisfiedbythememorycontrollerandnotbythehierarchy
instruction cache as well as a 2MiB L2 cache. An L3 cache of caches.
of6MiBandthememorycontrolleraresharedbythe8cores Tables1and2indicatethetotalexecutiontimeasafunc-
onthesamedie. The16KiBL1cacheisprivatetoeachcore. tion of the number of hops and the access mode of the syn-
Main memory is 64GiB, equally divided into 8 NUMA do- thetic benchmark for both systems. The results show that
mains. ForeachNUMAnode,4neighborsareatadistance latency increases with the distance between the requesting
of 1 hop and 3 neighbors are at 2 hops. core and the targeted memory controller and that writes
SGI-192 is an SGI UV2000 with 192 cores and 756GiB are significantly slower than reads. The rightmost column
RAM,distributedover24NUMAnodes,andrunningSUSE of each table shows the access time normalized to accesses
Linux Enterprise Server 11 SP3 with kernel 3.0.101-0.46- targeting local memory. For reads on the Opteron-64 sys-
default. The system is organized in blades, each of which tem, these values range from 1.81 for on-package accesses
contains two Intel Xeon E5-4640 CPUs running at 2.4GHz. to a memory controller at a distance of one hop to a factor
Each CPU has 8 cores with direct access to a memory con- of 4.34 for off-package accesses at a distance of two hops.
troller. The cache hierarchy consists of 3 levels: a core- For writes, these values are lower—1.2 to 2.48—due to the
private L1 with separate instruction and data cache, each higher latency of local writes. Not surprisingly, the fac-
with a capacity of 32KiB; a core-private, unified L2 cache torsforbothreadsandwritesaresignificantlyhigheronthe
of 256KiB; and a unified L3 cache of 20MiB, shared among larger SGI-192 system (up to 7.48 for reads at three hops).
all8coresoftheCPU.Hyperthreading wasdisabledforour This suggests that locality optimizations will have a higher
experiments. Each blade has a direct connection to a set impact on SGI-192 and that the locality of writes will have
of other blades and indirect connections to the remaining the greatest impact.
ones. From a core’s perspective, a memory controller can 4.3 Benchmarks
be either local if associated to the same CPU, at 1 hop if
on the same blade, at 2 hops if on a different blade that is We evaluate the impact of our techniques on nine bench-
connected directly to the core’s blade or at 3 hops if on a marks,eachofwhichisavailableinanoptimizedsequential
remote blade with an indirect connection. implementationandtwotunedparallelimplementationsus-
ing OpenStream.
LatencyofmemoryaccessesandNUMAfactors. The first parallel implementation uses task-private input
and output buffers as described in Section 2.1 and thus en-
We used a synthetic benchmark to measure the latency
ables enhanced work-pushing and deferred allocation. Data
of memory accesses as a function of the distance in hops
frominputbuffersisonlyreadandneverwritten,whiledata
between a requesting core and the memory controller that
in output buffers is only written and never read. Hence,
satisfies the request. It allocates a buffer on a given node
tasks cannot perform in-place updates and results are writ-
Readaccesses WriteaccessesFactorR/W Blocksize Blocksize
Local 1288.5±1.22ms2256.9±14.06ms 1.00/1.00 Matrix/Vectorsize (Opteron-64)(SGI-192) Iterations
1hop(on-package) 2328.4±0.49ms2717.6±12.16ms 1.81/1.20 Jacobi-1d 228 216 216 60
1hop(off-package)2781.4±0.56ms3934.6±00.56ms 2.16/1.74 Jacobi-2d 214×214 210×26 28×28 60
2hops 5601.6±0.57ms5601.3±00.55ms 4.34/2.48 Jacobi-3d 210×29×29 25×26×25 24×26×26 60
Seidel-1d 228 216 216 60
Table 1: Average latency of accesses on Opteron-64 Seidel-2d 214×214 28×28 27×29 60
Seidel-3d 210×29×29 26×25×25 24×28×24 60
Readaccesses WriteaccessesFactorR/W Blur- 215×215(Opteron-64) 29×26 -
Local 934.82±5.74ms 1307.4±2.95ms 1.00/1.00 roberts 216×216(SGI-192) 210×26
1hop 4563.1±3.02ms5282.38±1.56ms 4.88/4.04 Bitonic 228 216 217 -
2hops5820.48±2.11ms6473.38±1.16ms 6.23/4.95 K-means 40.96Mpts,10dims.,11clust.104 104 -
3hops6991.24±2.71ms7673.14±0.92ms 7.48/5.87
Table 3: Benchmark parameters
Table 2: Average latency of accesses on SGI-192
this baseline for our experiments as DSA-BASE. Identical
ten to a different location than the input data. We refer to
parameters for the block size and run-time have been used
this implementation as DSA (dynamic single assignment).
fortheexperimentswiththesharedmemoryversionsofthe
The second parallel implementation, which we refer to as
benchmarks (SHM), which we refer to as SHM-BASE.
SHM, uses globally shared data structures and thus does
AllbenchmarkswerecompiledusingtheOpenStreamcom-
notexposeinformationonmemoryaccessestotherun-time.
piler based on GCC 4.7.0. The compilation flags for blur-
However, the pages of the data structures are distributed
roberts aswellasthejacobi andseidel benchmarkswere-O3
across all NUMA nodes in a round-robin fashion using in-
-ffast-math, while kmeans uses -O3 and bitonic uses -O2.
terleavedallocation. Weusethisimplementationtocompare
The parallel implementations are provided with a paral-
our solutions to classical static NUMA-aware optimizations
lelcontrolprogramtopreventsequentialtaskcreationfrom
that require only minimal changes to the application. The
becomingaperformancebottleneck. Toavoidmemorycon-
benchmarks are the following.
troller contention, the initial and final data are stored in
• Jacobi-1d, jacobi-2d and jacobi-3d are the usual one-, globaldatastructuresallocatedusinginterleavedallocation
two- and three-dimensional Jacobi stencils iterating across all NUMA nodes.
overarraysofdoubleprecisionfloatingpointelements.
Ateachiteration,thealgorithmaveragesforeachma- Datadependencepatterns.
trixelementthevaluesoftheelementsinitsVonNeu-
The relevant producer-consumer patterns shown in Fig-
mannneighborhoodusingthevaluesfromtheprevious
ure 4 can be divided into three groups with different impli-
iteration.
cationsforouroptimizations: unbalanceddependences (e.g.,
• Seidel-1d,seidel-2dandseidel-3demployasimilarsten- one input buffer accounting for more than 90% of the in-
cil pattern but use values from the previous and the put data) with long dependence paths (jacobi-1d, jacobi-
current iteration for updates. 2d, jacobi-3d, seidel-1d, seidel-2d, seidel-3d, kmeans), un-
balanced dependences with short dependence paths (blur-
• Kmeans isadata-miningbenchmarkthatpartitionsa
roberts) and balanced dependences (bitonic). The behavior
set of n d-dimensional points into k clusters using the
ofourheuristicsonthesepatternsisreferencedintheexper-
K-means clustering algorithm. Each vector is repre-
imental evaluation. All of the benchmarks have non-trivial,
sented by d single precision floating point values.
connected task graphs, i.e., none of the benchmarks repre-
• Blur-robertsappliestwosuccessiveimagefiltersondou- sents an embarrassingly parallel workload.
ble precision floating point elements [22]: a Gaussian
blurfilter oneachpixel’sMooreneighborhoodfollowed Characterizationofmemoryaccesses.
by the Roberts Cross Operator for edge detection.
The benchmarks were carefully tuned (block sizes and
• Bitonic implements a bitonic sorting network [1], ap- tiling) to take advantage of caches. However, the effective-
plied to a sequence of arbitrary 64-bit integers. nessofthecachehierarchyalsodependsonthepattern,the
Table3summarizestheparametersforthedifferentbench- frequencyandthetimingofmemoryaccessesduringtheexe-
marksandmachines. Thesizeofinputdatawaschosentobe cutionofabenchmark,leadingtomoreorfewercachemisses
significantlyhigherthanthetotalamountofcachememory for a given block size. Figure 5 shows the cache miss rates
andlowenoughtopreventthesystemfromswapping. This at the last level of cache (LLC) on SGI-192 of DSA-BASE,
size is identical on both machines, except for blur-roberts, whichisagoodproxyfortherateofrequeststomainmem-
whoseexecutiontimeforimagesofsize215×215 istooshort ory for each benchmark. For all bar graphs in this paper,
on SGI-192 and which starts swapping for size 216×216 on error bars indicate standard deviation. As the focus of our
Opteron-64. Toamortizetheexecutionofauxiliarytasksat optimizations is on the locality of memory accesses, we ex-
the beginning and the end of execution of the stencils, we pectahigherimpactforbenchmarksexhibitinghigherLLC
set the number of iterations to 60. miss rates. For this reason, seidel and blur-roberts are ex-
The block size has been tuned to minimize the execution pectedtobenefitthemostfromouroptimizations,followed
timefortheparallelimplementationwithtask-privateinput by the jacobi benchmarks and bitonic. Kmeans has a very
andoutputdata(DSA)oneachmachine. Toavoidanybias low LLC miss rate and is not expected to show significant
in favor of our optimizations, enhanced work-pushing and improvement.
deferred allocation have been disabled during this tuning
4.4 Experimentalbaseline
phase. Inthisconfiguration,therun-timeonlyreliesonop-
timized work-stealing [23] extended with hierarchical work- Todemonstratetheeffectivenessofouroptimizations,our
stealing [14] for computational load balancing. We refer to principal point of comparison is DSA-BASE. We validate
(a)Jacobi-1d /seidel-1d (b)Jacobi-2d /seidel-2d (c)Jacobi-3d /seidel-3d
(d)Kmeans (e)Blur-roberts (f)Bitonic
Figure4: Maintasksandtypesofdependencesofthebenchmarks. Theamountofdataexchangedbetweentasksisindicated
by the width of arrows. Symbols: S , S , S : number of elements per block in x, y and z direction; S : number of
b,x b,y b,z b,p
points per block, d: number of dimensions, k: number of clusters in kmeans; S : number of elements per block in bitonic.
b
Figure 5: LLC misses per thousand instructions on SGI-192 Figure 6: Cholesky factorization on Opteron-64.
thesoundnessofthisbaselinebycomparingitsperformance Figure 7 shows the locality of requests to main memory
on Cholesky factorization against the two state-of-the-art RHW on Opteron-64. The configurations input only, output
loc
linear algebra libraries PLASMA/QUARK [35] and Intel’s only and weighted refer to DSA-BASE combined with the
MKL [18]. Figure 6 shows the execution times of Cholesky respectiveenhancedwork-pushingheuristic,whiledfa refers
factorizationonamatrixofsize8192×8192runningonthe toDSA-BASEcombinedwithdeferredallocation,butwith-
Opteron-64 platform with four configurations: DSA-BASE, out enhanced work-pushing. Data locality is consistently
Intel MKL, PLASMA and finally optimized OpenStream improved by our optimizations across all benchmarks. The
(DSA-OPT), the latter using our run-time implementing combination of enhanced work-pushing and deferred allo-
the optimizations presented in this paper. This validates cation (DSA-OPT) is comparable to the output only and
the soundness of our baseline, which achieves similar per- weightedheuristicsofwork-pushing,butyieldssignificantly
formancetoIntelMKL,whilealsoshowcasingtheeffective- better results than enhanced work-pushing only for bench-
ness of our optimizations, automatically and transparently marks with balanced dependences. For all jacobi and sei-
matchingtheperformanceofPLASMAwithoutanychange del benchmarksaswellaskmeans thelocalitywasimproved
in the benchmark’s source code. above88%andforbitonic above81%. Blur-roberts doesnot
benefit as much from our optimizations as the other bench-
marks. Astherun-timesystemonlymanagestheplacement
5. RESULTS ofprivatizeddataassociatedwithdependences,shortdepen-
Wenowevaluateenhancedwork-pushinganddeferredal- dencepaths,suchasinBlur-roberts,onlyallowtherun-time
location,startingwiththeimpactonmemoryaccesslocality, to optimize placement for a fraction of the execution. As a
and following on with the actual performance results. result, the overall impact is diluted proportionately. We
furthernotethattheinputonlyheuristicofwork-pushing—
5.1 Impactonthelocalityofmemoryaccesses closesttotheoriginalheuristicinpriorwork[14]—doesnot
improvememorylocalityasmuchasweightedwork-pushing
On the Opteron platform, we use two hardware perfor-
orthecombinationofdeferredallocationwithwork-pushing.
mancecounterstocounttherequeststonode-localmemory2
Figure 8 shows RRT on SGI-192, highlighting the effective-
andtoremotenodes,3 respectively. Weconsiderthelocality loc
ness of our optimizations on data under the control of the
metric RHW, defined as the ratio of local memory accesses
loc run-time. Not accounting for unmanaged data, we achieve
to total memory accesses, shown in Figure 7. We could not
almostperfectlocality(upto99.8%)acrossallbenchmarks,
providethecorrespondingfiguresfortheSGIsystemdueto
except for bitonic, where balanced dependences imply that
missing support in the kernel. However, the OpenStream
whenever input data is on multiple nodes, only half of the
run-time contains precise information on the working set of
input data can be accessed locally.
tasks and on the placement of input buffers, which can be
used to provide a second locality metric RRT that precisely
loc 5.2 Impactonperformance
accountsforaccessestodatamanagedbytherun-time,i.e.,
associated to task dependences. Figure 9 shows the speedup achieved over DSA-BASE.
Thebestperformanceisachievedbycombiningwork-pushing
2CPUIOREQUESTSTOMEMORYIO:LOCALCPUTOLOCALMEM and deferred allocation, with a global maximum of 2.5× on
3CPUIOREQUESTSTOMEMORYIO:LOCALCPUTOREMOTEMEM Opteron-64 and 5.0× on SGI-192. Generally, the speedups
Figure 7: Locality RHW of requests to main memory on the Opteron-64 system for deferred allocation
loc
Figure 8: Locality RRT of data managed by the run-time on SGI-192
loc
arehigheronSGI-192,showingthatouroptimizationshave withrespecttoNUMAandmigratesittowardsthenodeof
a higher impact on machines with higher penalties for re- the accessing CPU.
moteaccesses. Theseimprovementsresultfrombetterlocal- We first evaluate the influence of page migration on a
ity as well as from the memory footprint reduction induced synthetic benchmark to determine under which conditions
by deferred allocation. Note that the input-only heuristic the mechanism is beneficial and show that these conditions
did not perform well with jacobi, highlighting the impor- do not meet the requirements for task-parallel programs.
tance of considering both input and output flows, and of We then study the impact of dynamic page migration on
proactively distributing buffers through deferred allocation theSHMparallelbaselinewithgloballyshareddata(SHM-
rather than reactively adapting the schedule only. BASE).
6.1 Parametrizationofpagemigration
5.3 Comparisonwithinterleavedallocation For all experiments, we used version 4.3.0 of the Linux
Figure10showsthespeedupoftheDSAparallelbaseline Kernel. As the SGI test platform requires specific kernel
over the implementations using globally shared data struc- patchesandissharedamongmanyusers,weconductedthese
tures distributed over all NUMA nodes using interleaved experimentsonOpteron-64only. Themigrationmechanism
allocation (SHM-BASE). The optimizations achieve up to is configured through the procfs pseudo filesystem, as fol-
3.1× speedup on Opteron-64 and 5.6× on SGI-192. The lows:
bestperformanceissystematicallyobtainedbythecombined • Migration can be globally enabled or disabled by set-
work-pushinganddeferredallocationstrategy. Theseresults ting numa balancing to 1 or 0, respectively.
clearlyindicatethattakingadvantageofthedynamicdata-
• The parameter numa balancing scan delay ms indica-
flow information provided in modern task-dependent lan-
tes the minimum execution time of a process before
guagesallowsformoreprecisecontrolovertheplacementof
page migration starts. In our experiments, we have
data leading to improved performance over static schemes
set this value to 0toenable migrationas soonas pos-
unable to react to dynamic behavior at execution time. In
sible. Pagemigrationduringinitializationisprevented
thecaseofinterleavedallocation,theuniformaccesspattern
using appropriate calls to mbind, temporarily impos-
of the benchmarks evaluated in this work yields good load
ing static placement.
balancingacrossmemorycontrollers,butpoordatalocality.
• Theminimum/maximumdurationbetweentwoscans
6. COMPARISON WITH DYNAMIC PAGE iscontrolledbynuma balancing scan period min ms /
numa balancing scan period max ms. Wehavesetthe
MIGRATION
minimalperiodto0andthemaximumperiodto100000
Whileourstudyfocusedontheapplicationandrun-time, to allow for constant re-evaluation of the mapping.
the reader may wonder how kernel-level optimizations fare
• How much of the address space is examined in one
in our context. Recent versions of Linux comprise the bal-
scan is defined by numa balancing scan size mb. In
ancenuma patchset [12] for dynamic page migration and a
theexperiments,thisparameterhasbeensetto100000
transparent policy migrating pages during the execution of
to prevent the system from scanning only a subset of
a process based on its memory accesses. The kernel peri-
the pages.
odically scans the address space of the process and changes
the protection flags of the scanned pages such that an ac- In the following evaluation, we calculate the ratio of the
cess causes an exception. Upon an access to such a page, median wall clock execution time with dynamic migration
theexceptionhandlercheckswhetherthepageismisplaced (numa balancing setto1)dividedbythemediantimewith-
(a)Opteron-64system
(b)SGI-192system
Figure 9: Speedup over the parallel baseline DSA-BASE
(a)Opteron-64
(b)SGI-192
Figure10: Speedupovertheimplementationswithgloballyshareddatastructuresandinterleavingonallnodes(SHM-BASE)
out migration (numa balancing set to 0) for 10 runs of a 3. Assign exactly one buffer of the current set to each
synthetic benchmark. thread,withthreadibeingtheownerofthei-thbuffer.
6.2 Evaluationofasyntheticbenchmark 4. Synchronize all threads using a barrier and let each
thread traverse its buffer I times linearly by adding a
Thesyntheticbenchmarkhasbeendesignedspecificallyto
constant to the first 8-byte integer of every cache line
evaluatethepotentialofdynamicpagemigrationforscenar-
of the buffer.
ios with clear relationships between data and computations
and without interference. It is composed of the following 5. ChangetheaffinityAtimesbyrepeatingsteps3and4
steps: a total of A times.
6. Synchronize all threads with a barrier and print the
1. Allocate S sets of T 64MiB buffers, distributed in a
time elapsed between the moments in which the first
round-robin fashion on the machine’s NUMA node.
thread passed the first and the last barrier, respec-
That is, the i-th buffer of each set is allocated on
tively.
NUMAnode (imodN), withN beingthe totalnum-
ber of NUMA nodes.
On Opteron-64, the number of 8 cores per NUMA node is
equal to the total number of nodes. Thus, the allocation
2. Create T threads and pin the i-th thread on the i-th schemeabovecauseseveryNUMAnodetoaccesseverynode
core. ofthesystematthebeginningofeachaffinitychange,which