Hybrid distributed shared memory space in multi-core processors
2011 (English)In: Journal of Software, ISSN 1796-217X, Vol. 6, no 12 SPEC. ISSUE, 2369-2378 p.Article in journal (Refereed) Published
On multi-core processors, memories are preferably distributed and supporting Distributed Shared Memory (DSM) is essential for the sake of reusing huge amount of legacy code and easy programming. However, the DSM organization imports the inherent overhead of translating virtual memory addresses into physical memory addresses, resulting in negative performance. We observe that, in parallel applications, different data have different properties (private or shared). For the private data accesses, it's unnecessary to perform Virtual-to-Physical address translations. Even for the same datum, its property may be changeable in different phases of the program execution. Therefore, this paper focuses on decreasing the overhead of Virtualto- Physical address translation and hence improving the system performance by introducing hybrid DSM organization and supporting run-time partitioning according to the data property. The hybrid DSM organization aims at supporting fast and physical memory accesses for private data and maintaining a global and single virtual memory space for shared data. Based on the data property of parallel applications, the run-time partitioning supports changing the hybrid DSM organization during the program execution. It ensures fast physical memory addressing on private data and conventional virtual memory addressing on shared data, improving the performance of the entire system by reducing virtual-to-physical address translation overhead as much as possible. We formulate the run-time partitioning of hybrid DSM organization in order to analyze its performance. A real DSM based multi-core platform is also constructed. The experimental results of real applications show that the hybrid DSM organization with run-time partitioning demonstrates performance advantage over the conventional DSM counterpart. The percentage of performance improvement depends on problem size, way of data partitioning and computation/communication ratio of parallel applications, network size of the system, etc. In our experiments, the maximal improvement is 34.42%, the minimal improvement 3.68%.
Place, publisher, year, edition, pages
2011. Vol. 6, no 12 SPEC. ISSUE, 2369-2378 p.
Hybrid Distributed Shared Memory (DSM), Multi-core, Processor, Run-time Partitioning, Data partitioning, Data properties, Distributed shared memory, Entire system, Legacy code, Multi core, Multi-core platforms, Multi-core processor, Network size, Parallel application, Performance improvements, Physical address, Physical memory, Private data, Problem size, Program execution, Real applications, Runtimes, Shared data, Virtual memory, Virtual-to-physical address translations, Distributed computer systems, International trade, Parallel processing systems, Multicore programming
IdentifiersURN: urn:nbn:se:kth:diva-150652DOI: 10.4304/jsw.6.12.2369-2378ScopusID: 2-s2.0-83455211718OAI: oai:DiVA.org:kth-150652DiVA: diva2:746410
QC 201409122014-09-122014-09-082015-10-09Bibliographically approved