arXiv subjects

Thomas G. Robertazzi

Publications and source records attributed to Thomas G. Robertazzi.

6 recordsLinked to original sources

arXiv2026

Probabilistic Performance Analysis of Parallel Signature Search Strategies in Multi-Level Tree Networks

Hierarchical distributed search, locating a data pattern, or signature, across a tree-structured collection of files, underlies distributed index traversal, deep packet inspection and sequence alignment. A practitioner must decide how much parallelism to employ: scan each layer sequentially, fan out within subtrees, or launch the whole tree at once. Existing analyses answer this only partially: they characterize every node by the statistics of a signature-holding file and, for multi-signature files, need quantities revealed only at run time. We develop a probabilistic framework predicting the completion time of five search strategies, spanning sequential to full-tree parallelism, before any file is read. Node scan times are modeled as a mixture over signature presence, layer times as order statistics, and parallel subtree scans by extreme-value arguments; when signature counts are known, occupancy under capacity constraints is treated by generating functions. Each performance formula carries an exactness label: exact (or exact-in-regime), plug-in, asymptotic or bound, with each approximation quantified against Monte Carlo simulation and its regime identified. A multicore prototype reproduces the coarse separation between full-tree, layer- and subtree-level parallelism, but shows that synchronization overhead can erase the predicted separation between close strategies. The framework delivers a priori completion-time predictions with explicit accuracy regimes and negligible computational cost, the design example evaluated in under a millisecond; these timing models can support subsequent resource-cost optimization.

cs.DC↗

arXiv2025

Capacity Constraints in Ball and Urn Distribution Problems

This paper explores the distribution of indistinguishable balls into distinct urns with varying capacity constraints, a foundational issue in combinatorial mathematics with applications across various disciplines. We present a comprehensive theoretical framework that addresses both upper and lower capacity constraints under different distribution conditions, elaborating on the combinatorial implications of such variations. Through rigorous analysis, we derive analytical solutions that cater to different constrained environments, providing a robust theoretical basis for future empirical and theoretical investigations. These solutions are pivotal for advancing research in fields that rely on precise distribution strategies, such as physics and parallel processing. The paper not only generalizes classical distribution problems but also introduces novel methodologies for tackling capacity variations, thereby broadening the utility and applicability of distribution theory in practical and theoretical contexts.

math.PR↗

arXiv2021

Optimizing Data Intensive Flows for Networks on Chips

Data flow analysis and optimization is considered for homogeneous rectangular mesh networks. We propose a flow matrix equation which allows a closed-form characterization of the nature of the minimal time solution, speedup and a simple method to determine when and how much load to distribute to processors. We also propose a rigorous mathematical proof about the flow matrix optimal solution existence and that the solution is unique. The methodology introduced here is applicable to many interconnection networks and switching protocols (as an example we examine toroidal networks and hypercube networks in this paper). An important application is improving chip area and chip scalability for networks on chips processing divisible style loads.

cs.DC↗

arXiv2021

Optimal Signal Selection for Sensors

The focus of this research is sensor applications including radar and sonar. Optimal sensing means achieving the best signal quality with the least time and energy cost, which allows processing more data. This paper presents novel work by using an integer linear programming "algorithm" to achieve optimal sensing by selecting the best possible number of signals of a type or a combination of multiple types of signals to ensure the best sensing quality considering all given constraints. A solution based on a heuristic algorithm is implemented to improve the computing time performance. What is novel in this solution is synthesis of an optimized signal mix using information such as but not limited to signal quality, energy and computing time.

eess.SP↗

arXiv2021

Cloud Versus Local Processing in Distributed Networks

A method for evaluating the relative performance of local, cloud and combined processing of divisible (i.e. partitionable) data loads is presented. It is shown how to do this in the context of Amdahl's law. A single level (star) network operating under each of three fundamental scheduling policies is used as an example. Applications include mobile computing, cloud computing and signature searching.

cs.DC↗

arXiv2018

Layer Based Partition for Matrix Multiplication on Heterogeneous Processor Platforms

While many approaches have been proposed to analyze the problem of matrix multiplication parallel computing, few of them address the problem on heterogeneous processor platforms. It still remains an open question on heterogeneous processor platforms to find the optimal schedule that balances the load within the heterogeneous processor set while minimizing the amount of communication. A great many studies are based on rectangular partition, whereas the optimality of rectangular partition as the basis has not been well justified. In this paper, we propose a new method that schedules matrix multiplication on heterogeneous processor platforms with the mixed co-design goal of minimizing the total communication volume and the multiplication completion time. We first present the schema of our layer based partition (LBP) method. Subsequently, we demonstrate that our approach guarantees minimal communication volume, which is smaller than what rectangular partition can reach. We further analyze the problem of minimizing the task completion time, with network topologies taken into account. We solve this problem in both single-neighbor network case and multi-neighbor network case. In single-neighbor network cases, we propose an equality based method to solve LBP, and simulation shows that the total communication volume is reduced by 75% from the lower bound of rectangular partition. In multi-neighbor network cases, we formulate LBP as a Mixed Integer Programming problem, and reduce the total communication volume by 81% through simulation. To summarize, this is a promising perspective of tackling matrix multiplication problems on heterogeneous processor platforms.

cs.NI↗