Architecture and network-on-chip implementation of a new hierarchical interconnection network

A Midimew-connected Mesh Network (MMN) is a minimal distance mesh with wrap-around links network of multiple basic modules (BMs), in which the BMs are 2D-mesh networks that are hierarchically interconnected for higher-level networks. In this paper, we present the architecture of the MMN, addressing of node, routing of message, and evaluate the static network performance of MMN, TESH, mesh and torus networks. In addition, we propose the network-on-chip (NoC) implementation of MMN. With innovative combination of diagonal and hierarchical structure, the MMN possesses several attractive features, including constant degree, small diameter, low cost, small average distance, moderate bisection width and high fault tolerant performance than that of other conventional and hierarchical interconnection networks. The simple architecture of MMN is also highly suitable for NoC implementation. To implement all the links of level-3 MMN, only four layers are needed which is feasible with current and future VLSI technologies

Effect of ancilla's structure on quantum error correction using the 7-qubit Calderbank-Shor-Steane code

In this work we discuss the ability of different types of ancillas to control the decoherence of a qubit interacting with an environment. The error is introduced into the numerical simulation via a depolarizing isotropic channel. After the correction we calculate the fidelity as a quality criterion for the qubit recovered. We observe that a recovery method with a three-qubit ancilla provides reasonable good results bearing in mind its economy. If we want to go further, we have to use fault-tolerant ancillas with a high degree of parallelism, even if this condition implies introducing new ancilla verification qubits.Comment: 24 pages, 10 Figures included. Accepted in Phys. Rev. A 200

What does fault tolerant Deep Learning need from MPI?

Deep Learning (DL) algorithms have become the de facto Machine Learning (ML) algorithm for large scale data analysis. DL algorithms are computationally expensive - even distributed DL implementations which use MPI require days of training (model learning) time on commonly studied datasets. Long running DL applications become susceptible to faults - requiring development of a fault tolerant system infrastructure, in addition to fault tolerant DL algorithms. This raises an important question: What is needed from MPI for de- signing fault tolerant DL implementations? In this paper, we address this problem for permanent faults. We motivate the need for a fault tolerant MPI specification by an in-depth consideration of recent innovations in DL algorithms and their properties, which drive the need for specific fault tolerance features. We present an in-depth discussion on the suitability of different parallelism types (model, data and hybrid); a need (or lack thereof) for check-pointing of any critical data structures; and most importantly, consideration for several fault tolerance proposals (user-level fault mitigation (ULFM), Reinit) in MPI and their applicability to fault tolerant DL implementations. We leverage a distributed memory implementation of Caffe, currently available under the Machine Learning Toolkit for Extreme Scale (MaTEx). We implement our approaches by ex- tending MaTEx-Caffe for using ULFM-based implementation. Our evaluation using the ImageNet dataset and AlexNet, and GoogLeNet neural network topologies demonstrates the effectiveness of the proposed fault tolerant DL implementation using OpenMPI based ULFM

Wildcard dimensions, coding theory and fault-tolerant meshes and hypercubes

Hypercubes, meshes and tori are well known interconnection networks for parallel computers. The sets of edges in those graphs can be partitioned to dimensions. It is well known that the hypercube can be extended by adding a wildcard dimension resulting in a folded hypercube that has better fault-tolerant and communication capabilities. First we prove that the folded hypercube is optimal in the sense that only a single wildcard dimension can be added to the hypercube. We then investigate the idea of adding wildcard dimensions to d-dimensional meshes and tori. Using techniques from error correcting codes we construct d-dimensional meshes and tori with wildcard dimensions. Finally, we show how these constructions can be used to tolerate edge and node faults in mesh and torus networks

A new derivative of midimew-connected mesh network

In this paper, we present a derivative of Midimew connected Mesh Network (MMN) by reassigning the free links for higher level interconnection for the optimum performance of the MMN; called Derived MMN (DMMN). We present the architecture of DMMN, addressing of nodes, routing of message and evaluate the static network performance. It is shown that the proposed DMMN possesses several attractive features, including constant degree, small diameter, low cost, small average distance, moderate bisection width, and same fault tolerant performance than that of other conventional and hierarchical interconnection networks. With the same node degree, arc connectivity, bisection width, and wiring complexity, the average distance of the DMMN is lower than that of other networks