25,426 research outputs found
Sonet Network Design Problems
This paper presents a new method and a constraint-based objective function to
solve two problems related to the design of optical telecommunication networks,
namely the Synchronous Optical Network Ring Assignment Problem (SRAP) and the
Intra-ring Synchronous Optical Network Design Problem (IDP). These network
topology problems can be represented as a graph partitioning with capacity
constraints as shown in previous works. We present here a new objective
function and a new local search algorithm to solve these problems. Experiments
conducted in Comet allow us to compare our method to previous ones and show
that we obtain better results
Multi-criteria scheduling of pipeline workflows
Mapping workflow applications onto parallel platforms is a challenging
problem, even for simple application patterns such as pipeline graphs. Several
antagonist criteria should be optimized, such as throughput and latency (or a
combination). In this paper, we study the complexity of the bi-criteria mapping
problem for pipeline graphs on communication homogeneous platforms. In
particular, we assess the complexity of the well-known chains-to-chains problem
for different-speed processors, which turns out to be NP-hard. We provide
several efficient polynomial bi-criteria heuristics, and their relative
performance is evaluated through extensive simulations
Block-Diagonal and LT Codes for Distributed Computing With Straggling Servers
We propose two coded schemes for the distributed computing problem of
multiplying a matrix by a set of vectors. The first scheme is based on
partitioning the matrix into submatrices and applying maximum distance
separable (MDS) codes to each submatrix. For this scheme, we prove that up to a
given number of partitions the communication load and the computational delay
(not including the encoding and decoding delay) are identical to those of the
scheme recently proposed by Li et al., based on a single, long MDS code.
However, due to the use of shorter MDS codes, our scheme yields a significantly
lower overall computational delay when the delay incurred by encoding and
decoding is also considered. We further propose a second coded scheme based on
Luby Transform (LT) codes under inactivation decoding. Interestingly, LT codes
may reduce the delay over the partitioned scheme at the expense of an increased
communication load. We also consider distributed computing under a deadline and
show numerically that the proposed schemes outperform other schemes in the
literature, with the LT code-based scheme yielding the best performance for the
scenarios considered.Comment: To appear in IEEE Transactions on Communication
Heuristics for memory access optimization in embedded processors
Digital signal processors (DSPs) such as the Motorola 56k are equipped with two memory banks that are accessible in parallel in order to offer high memory bandwidth, which is required for high-performance applications. In order to make efficient use of the memory bandwidth offered by two or more memory banks, compilers for such DSPs should be capable of appropriately partitioning the program variables between the two memory banks and scheduling accesses. If two variables can be accessed simultaneously, then it is essential to have these two variables assigned to two different memory banks. Also if these two variables are in different banks, then instead of using two separate instructions for accessing the variables, both the accesses can be encoded into a single instruction, thereby reducing the code size as well. An efficient heuristic for maximizing the parallel accesses in DSPs with dual memory banks is proposed and evaluated. The heuristic is shown to be very effective on several examples. Architectures like the M3 DSP have a group memory for the single-instruction multiple-data (SIMD) architecture, for which addressing an element of the group means to access all the elements of that group in parallel, so there is no need for separately addressing each element of the group. Given a variable access sequence for a particular code, instead of separately accessing each one of the variables, if the variables are grouped then the number of memory accesses can be reduced as per SIMD paradigm. An efficient way of forming groups can significantly reduce the memory accesses. Two solutions for this problem are presented in this thesis. First, a novel integer linear programming formulation for forming the groups, thereby reducing the number of memory accesses in DSPs with SIMD architecture is presented. Second, a heuristic based on the solution for optimizing multiple memory bank accesses is presented and evaluated for this problem. Results on several graphs show the effectiveness of the heuristic
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