For the last decade, practical limitations have prevented processor speeds from increasing significantly. To increase throughput, the computing industry has turned to multiprocessing; that is, executing computations in parallel on separate processing units. Making use of these additional units requires support from the operating system (OS). Indeed, most modern operating systems do have the capability of recognizing and utilizing multiprocessor hardware. Pintos is an instructional operating system used by many institutions to teach important operating systems concepts. Pintos aims to increase student engagement by providing challenging programming projects in which students personally implement many core functionalities of an operating system. However, prior to this work, Pintos was a uniprocessor OS. This makes it difficult for Pintos to expose students to the same synchronization challenges that most modern kernel developers face. In addition, the first structured project, aimed at teaching scheduling policies, requires students to implement an uniprocessor variant of MLFQS scheduler which is no longer used in modern systems. We implemented Symmetric MultiProcessing (SMP) support in Pintos. We also created a new scheduling assignment to expose students to a multiprocessor proportional-share scheduling policy called Completely Fair Scheduler and to introduce them to the concept of load balancing. Finally, we evaluate the effectiveness of our new Pintos framework in augmenting students’ knowledge of OS scheduling and enhancing their ability to code and debug in a low-level environment. / Master of Science

While distributed memory systems have shaped the field of distributed systems for decades, the demand for many-core shared memory resources is increasing. Symmetric Multiprocessor Systems (SMPs) have become increasingly important recently among a wide array of disciplines, ranging from Bioinformatics to astrophysics, and beyond. With the increase in big data computing, the size and scope of traditional commodity server systems is often outpaced. While some big data applications can be mapped to distributed memory systems found through many cluster and cloud technologies today, this effort represents a large barrier of entry that some projects cannot cross. Shared memory SMP systems look to effectively and efficiently fill this niche within distributed systems by providing high throughput and performance with minimized development effort, as the computing environment often represents what many researchers are already familiar with. In this paper, we look at the use of two common shared memory systems, the ScaleMP vSMP virtualized SMP deployment at Indiana University, and the SGI UV architecture deployed at University of Arizona. While both systems are notably different in their design, their potential impact on computing is remarkably similar. As such, we look to compare each system first under a set of OpenMP threaded benchmarks via the SPEC group, and to follow up with our experience using each machine for Trinity de-novo assembly. We find both SMP systems are well suited to support various big data applications, with the newer vSMP deployment often slightly faster; however, certain caveats and performance considerations are necessary when considering such SMP systems.

Technological development has significantly reduced the distance between the performance of systems designed using reconfigurable computing and dedicated hardware. The main sources of performance are the high density level of the FPGAs and the resources? improvement offered by manufacturers, who make more its use more attractive in a variety of applications, emphatically in systems that demand a high degree of flexibility. In this context, the objective of this work consists on the exploration of the resources offered by FPGAs for the development of a multiprocessed platform with the purpose of parallel execution of tasks. In this way, the eCos operating system was modified, with the addition of new characteristics to support of the Symmetric Multiprocessing model, using three soft-Core Altera Nios II processors. On this operating system, all parallelism is directly related to execution of the threads. This platform was analyzed and validated through the execution of parallel algorithms, emphasizing aspects of performance and flexibility compared to other architectures. This work contributes for reaching better results in the execution of tasks in robotics area, which belongs to a domain that demand great competition of tasks, mainly in modules that involve interaction with the external environment / Technological development has significantly reduced the distance between the performance of systems designed using reconfigurable computing and dedicated hardware. The main sources of performance are the high density level of the FPGAs and the resources? improvement offered by manufacturers, who make more its use more attractive in a variety of applications, emphatically in systems that demand a high degree of flexibility. In this context, the objective of this work consists on the exploration of the resources offered by FPGAs for the development of a multiprocessed platform with the purpose of parallel execution of tasks. In this way, the eCos operating system was modified, with the addition of new characteristics to support of the Symmetric Multiprocessing model, using three soft-Core Altera Nios II processors. On this operating system, all parallelism is directly related to execution of the threads. This platform was analyzed and validated through the execution of parallel algorithms, emphasizing aspects of performance and flexibility compared to other architectures. This work contributes for reaching better results in the execution of tasks in robotics area, which belongs to a domain that demand great competition of tasks, mainly in modules that involve interaction with the external environment

Technological development has significantly reduced the distance between the performance of systems designed using reconfigurable computing and dedicated hardware. The main sources of performance are the high density level of the FPGAs and the resources? improvement offered by manufacturers, who make more its use more attractive in a variety of applications, emphatically in systems that demand a high degree of flexibility. In this context, the objective of this work consists on the exploration of the resources offered by FPGAs for the development of a multiprocessed platform with the purpose of parallel execution of tasks. In this way, the eCos operating system was modified, with the addition of new characteristics to support of the Symmetric Multiprocessing model, using three soft-Core Altera Nios II processors. On this operating system, all parallelism is directly related to execution of the threads. This platform was analyzed and validated through the execution of parallel algorithms, emphasizing aspects of performance and flexibility compared to other architectures. This work contributes for reaching better results in the execution of tasks in robotics area, which belongs to a domain that demand great competition of tasks, mainly in modules that involve interaction with the external environment / Technological development has significantly reduced the distance between the performance of systems designed using reconfigurable computing and dedicated hardware. The main sources of performance are the high density level of the FPGAs and the resources? improvement offered by manufacturers, who make more its use more attractive in a variety of applications, emphatically in systems that demand a high degree of flexibility. In this context, the objective of this work consists on the exploration of the resources offered by FPGAs for the development of a multiprocessed platform with the purpose of parallel execution of tasks. In this way, the eCos operating system was modified, with the addition of new characteristics to support of the Symmetric Multiprocessing model, using three soft-Core Altera Nios II processors. On this operating system, all parallelism is directly related to execution of the threads. This platform was analyzed and validated through the execution of parallel algorithms, emphasizing aspects of performance and flexibility compared to other architectures. This work contributes for reaching better results in the execution of tasks in robotics area, which belongs to a domain that demand great competition of tasks, mainly in modules that involve interaction with the external environment