Concurrent Data Structures in Architectures with Limited Shared Memory Support
Paper i proceeding, 2014

The Single-chip Cloud Computer (SCC) is an experimental multicore processor created by Intel Labs for the many-core research community, to study many-core processors, their programmability and scalability in connection to communication models. It is based on a distributed memory architecture that combines fast-access, small on-chip memory with large off-chip private and shared memory. Additionally, its design is meant to favour message-passing over the traditional shared-memory programming. To this effect, the platform deliberately does not provide hardware supported cache-coherence or atomic memory read/write operations across cores. Because of these limitations of the hardware support, algorithmic designs of concurrent data structures in the literature are not suitable. In this paper, we delve into the problem of designing concurrent data structures on such systems. By utilising their very efficient message-passing together with the limited shared memory available, we provide two techniques that use the concept of a coordinator and one that combines local locks with message passing. All three achieve high concurrency and resiliency. These techniques allow us to design three efficient algorithms for concurrent FIFO queues. Our techniques are general and can be used to implement other concurrent abstract data types. We also provide an experimental study of the proposed queues on the SCC platform, analysing the behaviour of the throughput of our algorithms based on different memory placement policies.


Ivan Walulya

Chalmers, Data- och informationsteknik, Nätverk och system

Ioannis Nikolakopoulos

Chalmers, Data- och informationsteknik, Nätverk och system

Marina Papatriantafilou

Chalmers, Data- och informationsteknik, Nätverk och system

Philippas Tsigas

Chalmers, Data- och informationsteknik, Nätverk och system

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

03029743 (ISSN) 16113349 (eISSN)

Vol. 8805 Part 1 189-200
978-3-319-14325-5 (ISBN)


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