AN EXTENDED OPENMP TARGETING ON THE HYBRID ARCHITECTURE

OF SMP-CLUSTER

AuthorAuthor ：： Y. ZhaoY. Zhao 、 、 C. HuC. Hu 、、 S. WangS. Wang 、 、 S. ZhangS. ZhangSource Source ：： Proceedings of the 2nd IASTED international Proceedings of the 2nd IASTED international

conference on Advances in computer science conference on Advances in computer science and technology and technology

Speaker Speaker ： ： Cheng-Jung WuCheng-Jung Wu

OutlineOutline

Introduction Extensions in EOMP

Computing Resource Definition Hierarchical Data Layout and Data Mapping

Execution Model for EOMP Experiments and Results

Dot Product Matrix multiplication under EOMP execution mode

l on SMP cluster Conclusion

Introduction Clusters of shared-memory multiprocessors (SMPs)

More and more popular in High Performance Computing area

SMP clusters’ hybrid architectures Supports for a wide range of parallel paradigm

Three programming paradigms

Standard message passing Hybrid paradigm corresponding to the underlying architecture Shared memory paradigm built on a Software Distribute Softwar

e Memory (SDSM)

Three major metrics Performance、 Portability 、 Programmability

Introduction

None of the three parallel programming paradigms can meet on all of the three metrics

New parallel paradigm (EOMP) A compromising model

Balance the three major metrics Features

Good programmability Acceptable performance

Improve memory behavior Data locality The programs running on SMP cluster

Inter-node and intra-node data locality

Extensions in EOMP

Since OpenMP Shared-memory systems Lacks the support for distributed memory system

New directives Computing resource definition Data mapping

Computing Resource Definition

Definitions Virtual node (VN) Virtual processor (VP)

VNs Physical nodes Target units of inter-node data distribution

VPs Physical processors Target units of intra-node data reallocation and task s

cheduling during compilation

Computing Resource Definition

Semantics of computing resource definition directives Examples for processor mapping are given

Hierarchical Data Layout and Data Mapping ： Inter-node Data Mapping

Scalar data defined in the EOMP Shared data at default

Every node gets an own copy of the data

Inter-node task parallel Allows the shared scalar data be modified in certain nodes

Global addresses of distributed arrays Llocal addresses

Inter-node data mapping distributes the mapped arrays to VNs

Semantics for inter-node data distribution directive: #pragma eomp distribute a (BLOCK*) onto N

Hierarchical Data Layout and Data Mapping ： Intra-node Data Mapping

Shared memory data layout takes the advantage of global address

Technically No further data mapping is required inside the nodes

In certain cases Improper order of data access would decrease cache performance

false sharing or long-stride access

For instance： two threads always access the nearby array elements in memory at same time cache performance may be very poor due to severe false sharing

Optimizations for intra-node data layout will be necessary

Hierarchical Data Layout and Data Mapping ： Intra-node Data Mapping

An extreme example Experiment on that circumstance shows an overall 90% reductio

n of L1 cache miss after the intra-node data reallocation optimization

(On 4-cpu IA64 SMP; the array a is of 1M size).

Hierarchical Data Layout and Data Mapping ： Intra-node Data Mappin

g Two strategies can be adopted to reduce cache miss

Rearrange the access order of each thread Not always possible for compiler optimization It depends closely on the source program structure In the interleaving data case above, this means to avoid accessing t

he neighboring data in memory at the same time.

Reallocate the data layout in memory, Not change data dependencies of the source program Assures the correctness of this optimization Store the data that accessed by the same thread in a contiguous me

mory block

Hierarchical Data Layout and Data Mapping ： Intra-node Data Mapping Intra-node data reallocation

programmer-specified directives compiler reference analysis

Intra-node data reallocating data in memory Additional time and space overheads Evaluating the performance speedup of this optimization The data locations have been changed

The reallocated data should be forbidden

Semantics for intra-node data reallocation directive #pragma eomp distribute a (CYCLIC,*) intra

Execution Model for EOMP

Inter-node barriers and broadcasts Modifications of shared vari

ables in the parallel section at the edges of task parallel region

Maintain data consistency

Inter-node communications use explicit message passing

Execution Model for EOMP

Massage passing & multithreading program generated By compiler first distributes d

ata and schedule the tasks across nodes

Then deals with the intra-node data reallocation and task scheduling

Experiments and Results ： Dot Product

Experiments and Results ： Dot Product

The experiment result shows that the efficiency of the EOMP based on the runtime library is similar to the MPI+OpenMP program (better under some cases)

But not good as pure MPI, because the amount of calculations in the dot product operation are not enough, comp

aring to the cost of inside-node scheduling

Matrix multiplication under EOMP execution model on SMP cluster

C=A*B A and C is distributed in rows B is distributed in columns

Matrix multiplication under EOMP execution model on SMP cluster

Matrix size is small The cost of inter-node scheduling and communications are relati

vely high (compared with the computation cost) The three distributed memory models can not acquire a speedup

Matrix size becomes larger The three distributed memory models achieve reasonable speed

ups

Notice that the EOMP model after intra-node data reallocation Gets a high speedup when the matrix size is large Showing that the improved intra-node cache performance can gr

eatly benefit the overall performance of the program on SMP clusters

Matrix multiplication under EOMP execution model on SMP cluster

Peaks of EOMP-INDR curves in 500*500 and 1000*1000 cases The effect of data reallocation is related with both the size of cac

he line local b As the nodes become more and more, the size of local b on eac

h node becomes smaller That means the cache line may fill in more rows of local b, thus t

he cache misses is reduced Explain why the peak in 500*500 multiplication case comes earli

er than that of the 1000*1000 case

Matrix multiplication under EOMP execution model on SMP cluster

Conclusion

The experiment result Feasibility of our execution model The benefit gained from intra-node data reallocation

For future work, we plan to develop a complete source to source EOMP compiler Be based on ORC (Open Resource Compiler for IA64) Our current runtime library prototype Focusing on the communication generation and data manageme

nt.