Vector class on limited local memory (LLM) multi-core processors

Bai, Ke; Lu, Di; Shrivastava, Aviral

doi:10.1145/2038698.2038731

Cited by 10 publications

(2 citation statements)

References 15 publications

(15 reference statements)

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“…Recent research results have been quite encouraging. Techniques have been proposed to manage all kinds of data: code [4,19], stack [8,20], and heap [3,[5][6][7] efficiently on the SPMs of the core. In fact, [4] and [20] show that the overhead of code and stack management on SPMs is lower than on a cache based architecture.…”

Section: Introductionmentioning

confidence: 99%

Construction of GCCFG for inter-procedural optimizations in software managed manycore (SMM) architectures

Holton

Bai

Shrivastava

et al. 2014

Proceedings of the 2014 International Conference on Compilers, Architecture and Synthesis for Embedded Systems

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Software Managed Manycore (SMM) architectures -in which each core has only a scratch pad memory (instead of caches), -are a promising solution for scaling memory hierarchy to hundreds of cores. However, in these architectures, the code and data of the tasks mapped to the cores must be explicitly managed in the software by the compiler. State-ofthe-art compiler techniques for SMM architectures require inter-procedural information and analysis. A call graph of the program does not have enough information, and Global CFG, i.e., combining all the control flow graphs of the program has too much information, and becomes too big. As a result, most new techniques have informally defined and used GCCFG (Global Call Control Flow Graph) -a whole program representation which captures the control-flow as well as function call information in a succinct way -to perform inter-procedural analysis. However, how to construct it has not been shown yet. We find that for several simple call and control flow graphs, constructing GCCFG is relatively straightforward, but there are several cases in common applications where unique graph transformation is needed in order to formally and correctly construct the GCCFG. This paper fills this gap, and develops graph transformations to allow the construction of GCCFG in (almost) all cases. Our experiments show that by using succinct representation (GCCFG) rather than elaborate representation (GlobalCFG), the compilation time of state-of-the-art code management technique [4] can be improved by an average of 5X, and that of stack management [20] can be improved by an average of 4X.

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Section: Introductionmentioning

confidence: 99%

Construction of GCCFG for inter-procedural optimizations in software managed manycore (SMM) architectures

Holton

Bai

Shrivastava

et al. 2014

Proceedings of the 2014 International Conference on Compilers, Architecture and Synthesis for Embedded Systems

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“…stack data [10,29] and heap data ( [6,8,9] for its form in C, [7] for its form in C++) on the cores with only scratchpad memories. Of these techniques, developing efficient approaches to manage stack data is especially important, since an average of 64% of all accesses in embedded applications may be to stack variables [16].…”

Section: Introductionmentioning

confidence: 99%

SSDM

Lü

Bai

Shrivastava

2013

Proceedings of the 50th Annual Design Automation Conference

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Software Managed Multicore (SMM) architectures have been proposed as a solution for scaling the memory architecture. In an SMM architecture, there are no caches, and each core has only a local scratchpad memory. If all the code and data of the task to be executed on an SMM core cannot fit on the local memory, then data must be managed explicitly in the program through DMA instructions. While all code and data need to be managed, an efficient technique to manage stack data is of utmost importance since an average of 64% of all accesses may be to stack variables [16]. In this paper, we formulate the problem of stack data management optimization on an SMM core. We then develop both an ILP and a heuristic -SSDM (Smart Stack Data Management) to find out where to insert stack data management calls in the program. Experimental results demonstrate SSDM can reduce the overhead by 13X over the state-of-the-art stack data management technique [10].

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