MaRS, a combinator graph reduction multiprocessor

Contessa, Alessandro; Cousin, Eric; Coustet, C.; Cubero-Castan, Manuel; Durrieu, Guy; Lecussan, B.; Lemaı̂tre, Michel; Ng, Paulino

doi:10.1007/3540512845_39

Cited by 3 publications

(1 citation statement)

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“…It should be interpreted in light of the development examples presented in this article which use planar graphs, simple enough to be convincing. (2) In the MaRS data-flow machine [25], task density has been used for load balancing, which is similar to a repulsive force that distributes density evenly. But this example uses an all-to-all network, resulting in a non-scalable single space where each pair of PEs is at the same distance.…”

Section: The Blob Approachmentioning

confidence: 99%

The foundation of self-developing blob machines for spatial computing

Gruau

Eisenbeis²,

Maignan³

2008

Physica D: Nonlinear Phenomena

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The current trend in electronics is to integrate more and more transistors on a chip and produce massive hardware resources. As a consequence, traditional computing models, which mainly compute in the temporal domain, do not work well anymore since it becomes increasingly difficult to orchestrate these massive-scale hardware resources in a centralized way. Spatial computing is a unifying term that embodies many unconventional computing models and means computing on a relatively homogeneous physical medium made of hardware components, where the communication time is dependent on the Euclidean distance between the components (locality constraint). This constraint makes the programming for high performance significantly more complex compared to classical non-spatial hardware because performance now depends on where computation happens in space (mapping problem). Blob computing is a new approach that addresses this parallel computing challenge in a radically new and unconventional way: it decouples the mapping of computations onto the hardware from the software programming while still elegantly exploiting the space of the underlying hardware. Hardware mapping of computations is done by a physical force-based approach that simulates forces between threads of computation (automata). Attractive forces are used to keep automata that need to communicate with each other closer while repulsive forces are used for load balancing. The advantage of these primitives is that they are simple enough to be implemented on an arbitrary computing medium. They form the basis of a runtime system (RTS) that transforms an arbitrary computing medium into an easier-toprogram virtual machine called the blob machine. The basic objects of the blob machine are those automata, and the instructions let automata create new automata in specific ways so as to maintain a hierarchical organization (which facilitates both the mapping and the programming). We detail the basic instructions of the blob machine and demonstrate their confluence. Programming a spatial medium to perform a given algorithm then boils down to programming the blob machine, provided the RTS is implemented on it. The advantage of this approach is the hardware independency, meaning that the same program can be used on different media. By means of several examples programmed using a high-level language description, we further show that we can efficiently implement most current parallel computing models, such as Single Instruction Multiple Data (SIMD), data parallelism, "divide-and-conquer" parallelism and pipelining which demonstrates parallel expressiveness. On sorting and matrix multiplication algorithms, we also show that our approach scales up optimally with the number of basic hardware components.

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Section: The Blob Approachmentioning

confidence: 99%