A new iterative algorithm, the multi-level algorithm, for the numerical solution of steady state Markov chains is presented. The method utilizes a set of recursively coarsened representations of the original system to achieve accelerated convergence. It is motivated by multigrid methods, which

We propose new approaches to ray tracing that greatly reduce the required number of operations while strictly preserving the geometrical correctness of the solution. A hierarchical “beam” structure serves as a proxy for a collection of rays. It is tested against a kd-tree representing the overall

the flow of data through the memory hierarchy. Our data-centric transformations permit a more direct solution to the problem of enhancing data locality than current control-centric techniques do, and generalize easily to multiple levels of memory hierarchy. We buttress these claims with performance numbers

Complexity theory of circuits strongly suggests that deep architectures can be much more efficient (sometimes exponentially) than shallow architectures, in terms of computational elements required to represent some functions. Deep multi-layer neural networks have many levels of non

Power delivery, electricity consumption, and heat management are becoming key challenges in data center environments. Several past solutions have individually evaluated different techniques to address separate aspects of this problem, in hardware and software, and at local and global levels

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given circuit. In this paper, a method is proposed to solve the hazard-free multi-level existence problem. The method is proven to be both sound and complete for a large class of multi-level implementations. A novel contribution is to show that, if any hazard-free multi-level solution exists, then a

within the same level of the cache. We show that, for multi-megabyte level-two caches, an adaptive, dynamic NUCA design achieves 1.5 times the IPC of a Uniform Cache Architecture of any size, outperforms the best static NUCA scheme by 11%, outperforms the best three-level hierarchywhile using less

by unifying region and boundary-based information as an improved Geodesic Active Contour Model. The defined objective function is minimized using a gradient-descent method where a level set approach is used to implement the obtained PDE. According to this PDE, the curve propagation towards the final solution

ofthis solution is that it is possible, in a cache-main memory subsystem, to delay updating the main memory until a block is needed in the cache (nonstore-through mode of operation). Index Terms-Caches, coherence, memory hierarchy, multi-processor systems, nonstore-through. I.