The Non-inclusive Diagnosability of Hypercubes under the MM* Model

Diagnosability is an important factor in multiple-processor systems defined as the maximum number of faulty nodes that a system can recognize. In this paper, we propose a new form of diagnosability called non-inclusive diagnosability that requires all faulty sets to be non-inclusive. Furthermore, we study the non-inclusive diagnosability of hypercubes under the MM* model for [Formula: see text].

DrPin: A dynamic binary instumentator for multiple processor architectures

Modern applications rely heavily on dynamically loaded shared libraries, making static analysis tools used to debug and understand applications no longer sufficient. As a consequence, dynamic analysis tools are being adopted and integrated into the development and study of modern applications. Building tools that manipulate and instrument binary code at runtime is difficult and error-prone. Because of that, Dynamic Binary Instrumentation (DBI) frameworks have become increasingly popular. Those frameworks provide means of building dynamic binary analysis tools with low effort. Among them, Pin 2 has been by far the most popular and easy to use one. However, since the release of the Linux Kernel 4 series, it became unsupported, and Pin 3 broke backward compatibility. In this work we focus on studying the challenges faced when building a new DBI (DrPin) that seeks to be compatible with Pin 2 API, without the restrictions of Pin 3, that also runs multiple architectures (x86-64, x86, Arm, Aarch64), and on modern Linux systems.

Multiple Processor Scheduling with Optimum Execution Time and Processor Utilization Based on the SOSA

The multiple processor scheduling problem characterizes that different processor comprises of an arrangement of jobs or tasks designate proficient utilizing a limited number of processors. Herein development a multi-objective algorithm utilizing Symbiotic Organisms Search algorithm (SOSA) for scheduling an arrangement of reliant on tasks on obtainable resources in a multiple processor environment which minimizes the execution time and maximize the processor utilization. SOSA is a nature-inspired meta-heuristic algorithm utilized to compare with other meta-heuristic algorithms such as Water cycle algorithm (WCA), Genetic algorithm based Bacteria foraging optimization (GBF), Bacteria Foraging Optimization (BFO) and Genetic Algorithm (GA). SOSA reproduces the advantageous association methodologies received by life forms to survive and engender in the biological-community (ecosystem). Based on experimental results, we find the execution time as well as processor utilization using SOSA technique and then compare with the other mentioned algorithms. Acquired outcomes affirm the incredible execution of the SOSA in solving the multiple processor scheduling problems.

Large-scale building simulation using cloud computing for estimating lifecycle energy consumption

The use of whole building simulation is increasing to support the design process. Often it is desirable to evaluate many scenarios, however the simulation time involved presents a significant barrier. Simulationists are forced to reduce the number of scenarios evaluated to meet time constraints. With cloud computing, simulationists can significantly reduce the total simulation time by allocating portions of the simulations to multiple processor cores. The benefit of cloud computing is demonstrated through a case study project, which computes the lifecycle energy consumption (LEC) of 1 080 000 single detached home design scenarios in Toronto, for a budget of CAN$2400. Code was written using Python to couple EnergyPlus and ATHENA IE to modify input files, process results and calculate LEC. The results of this study suggest that utilizing cloud computing to simulate large scenario studies represents an efficient method that is beginning to surface in mainstream building simulation.