"We clarified that, although originally developed from the domain of ML, fractal parallel computing is fairly generally applicable," the researchers concluded, drawing from their preliminary results that FPM, general-purpose and cost-optimal as it is, is as powerful as many fundamental parallel computing models such as BSP and alternating Turing machine. The samples covered embarrassingly parallel, divide-and-conquer, and dynamic programming algorithms, all of which were demonstrated as efficiently programmable. Meanwhile, the researchers proposed two different ML-targeting FvNA architectures-the specific Cambricon-F and the universal Cambricon-FR-and illustrated the fractal programming style of FPM by running several general-purpose sample programs. Therefore, FPM cannot be programmed to be scale-dependent by definition. The processor is only aware of its parent component and child components, but not the global system specification." In other words, the program never knows where it resides in the tree structure. "What is more important, FPM puts explicit restrictions on the programming by only exposing a single processor to the programming interface. "Compared with Valiant's multi-BSP, FPM minimized the parameters for simpler abstraction," the researchers said. Components can execute fracops-the scheme of payloads on fractal parallel computing systems, such as reading some input data from the external storage, performing computation on the processor, and then writing output data to the external storage. FPM was built on Valiant's multi-BSP, a homogeneous multilayered parallel model, with only minor extensions.Īn instance of FPM is a tree structure of nested components each component contains a memory, a processor, and child components. To answer these questions, the researchers started by modeling the fractal parallel machine (FPM), an abstract parallel computer modeled from FvNA. If so, what are the exact prerequisites?.Is FvNA also applicable to payloads from other domains?.How could FvNA remain quite efficient with such a strict architectural constraint?.In this paper, the following three were addressed: Therefore, ML computers built with FvNA are programmable under a scale-invariant, homogeneous, and sequential view," the researchers explained.Īlthough FvNA has been testified as applicable to the ML domain and capable of alleviating the programming productivity issue while functioning efficiently as its ad hoc counterparts, some problems remain to be solved. "The lower layer is fully controlled by the higher layer, thus, only the top layer is exposed to the programmer as a monolithic processor. That is, just the opposite of the conventional anisostratal ML computer architecture, FvNA adopts the same instruction-set architecture (ISA) for every layer. If a system is "fractal," according to the researchers, it implies that the system always uses the same program regardless of the scale.įvNA, a multilayered, parallelized von Neumann architecture, is not only fractal but also isostratal-which literally means "same across layered structures". "Fractalness" is a borrowed geometric concept that describes the self-similar patterns applied to any scale. "Addressing the productivity issue, we proposed ML computers with fractal von Neumann architecture (FvNA)," said Yongwei Zhao, researcher from the State Key Lab of Processors, Institute of Computing Technology of CAS. To solve the problem, researchers from the Chinese Academy of Sciences (CAS) proposed a fractal parallel computing model and published their research in Intelligent Computing on Sept. Dedicated ML computers are thus being developed at various scales, but their productivity is somewhat limited: the workload and development cost are largely concentrated in their software stacks, which need to be developed or reworked on an ad hoc basis to support every scaled model.
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