Using Memory Access Traces to Map Threads and Data on Hierarchical Multi-core Platforms

In parallel programs, the tasks of a given application must cooperate in order to accomplish the required computation. However, the communication time between the tasks may be different depending on which core they are executing and how the memory hierarchy and interconnection are used. The problem...

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Bibliographic Details
Main Authors: Molina da Cruz, Eduardo Henrique, Zanata Alves, Marco Antonio, Carissimi, A., Navaux, P. O. A., Ribeiro, C. P., Mehaut, J.-F
Format: Conference Proceeding
Language:English
Subjects:
Benchmark testing
Instruction sets
Linux
Measurement
Memory management
Multicore processing
Online Access:Request full text
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Summary:In parallel programs, the tasks of a given application must cooperate in order to accomplish the required computation. However, the communication time between the tasks may be different depending on which core they are executing and how the memory hierarchy and interconnection are used. The problem is even more important in multi-core machines with NUMA characteristics, since the remote access imposes high overhead, making them more sensitive to thread and data mapping. In this context, process mapping is a technique that provides performance gains by improving the use of resources such as interconnections, main memory and cache memory. The problem of detecting the best mapping is considered NP-Hard. Furthermore, in shared memory environments, there is an additional difficulty of finding the communication pattern, which is implicit and occurs through memory accesses. This work aims to provide a method for static mapping for NUMA architectures which does not require any prior knowledge of the application. Different metrics were adopted and an heuristic method based on the Edmonds matching algorithm was used to obtain the mapping. In order to evaluate our proposal, we use the NAS Parallel Benchmarks (NPB) and two modern multi-core NUMA machines. Results show performance gains of up to 75% compared to the native scheduler and memory allocator of the operating system.
ISSN:1530-2075
DOI:10.1109/IPDPS.2011.197