Decision-theoretic exploration of multiProcessor platforms

In this paper, we present an efficient technique to perform design space exploration of a multi-processor platform that minimizes the number of simulations needed to identify the power-performance approximate Pareto curve. Instead of using semi-random search algorithms (like simulated anneal-ing, tabu search, genetic algorithms, etc.), we use domain knowledge derived from the platform architecture to set-up exploration as a decision problem. Each action in the decision-theoretic framework corresponds to a change in the platform parameters. Simulation is performed only when information about the probability of action outcomes be-comes insufficient for a decision. The algorithm has been tested with two multi-media industrial applications, namely an MPEG4 encoder and an Ogg-Vorbis decoder. Results show that the exploration of the number of processors and two-level cache size and policy, can be performed with less than 15 simulations with 95 % accuracy, increasing the ex-ploration speed by one order of magnitude when compared to traditional operation research techniques. 1

Parallel Architectures for Planetary Exploration Requirements (PAPER)

The Parallel Architectures for Planetary Exploration Requirements (PAPER) project is essentially research oriented towards technology insertion issues for NASA's unmanned planetary probes. It was initiated to complement and augment the long-term efforts for space exploration with particular reference to NASA/LaRC's (NASA Langley Research Center) research needs for planetary exploration missions of the mid and late 1990s. The requirements for space missions as given in the somewhat dated Advanced Information Processing Systems (AIPS) requirements document are contrasted with the new requirements from JPL/Caltech involving sensor data capture and scene analysis. It is shown that more stringent requirements have arisen as a result of technological advancements. Two possible architectures, the AIPS Proof of Concept (POC) configuration and the MAX Fault-tolerant dataflow multiprocessor, were evaluated. The main observation was that the AIPS design is biased towards fault tolerance and may not be an ideal architecture for planetary and deep space probes due to high cost and complexity. The MAX concepts appears to be a promising candidate, except that more detailed information is required. The feasibility for adding neural computation capability to this architecture needs to be studied. Key impact issues for architectural design of computing systems meant for planetary missions were also identified

Civil space technology initiative

The Civil Space Technology Initiative (CSTI) is a major, focused, space technology program of the Office of Aeronautics, Exploration and Technology (OAET) of NASA. The program was initiated to advance technology beyond basic research in order to expand and enhance system and vehicle capabilities for near-term missions. CSTI takes critical technologies to the point at which a user can confidently incorporate the new or expanded capabilities into relatively near-term, high-priority NASA missions. In particular, the CSTI program emphasizes technologies necessary for reliable and efficient access to and operation in Earth orbit as well as for support of scientific missions from Earth orbit

Fuzzy logic based energy and throughput aware design space exploration for MPSoCs

Multicore architectures were introduced to mitigate the issue of increase in power dissipation with clock frequency. Introduction of deeper pipelines, speculative threading etc. for single core systems were not able to bring much increase in performance as compared to their associated power overhead. However for multicore architectures performance scaling with number of cores has always been a challenge. The Amdahl's law shows that the theoretical maximum speedup of a multicore architecture is not even close to the multiple of number of cores. With less amount of code in parallel having more number of cores for an application might just contribute in greater power dissipation instead of bringing some performance advantage. Therefore there is a need of an adaptive multicore architecture that can be tailored for the application in use for higher energy efficiency. In this paper a fuzzy logic based design space exploration technique is presented that is targeted to optimize a multicore architecture according to the workload requirements in order to achieve optimum balance between throughput and energy of the system

A Survey and Comparative Study of Hard and Soft Real-time Dynamic Resource Allocation Strategies for Multi/Many-core Systems

Multi-/many-core systems are envisioned to satisfy the ever-increasing performance requirements of complex applications in various domains such as embedded and high-performance computing. Such systems need to cater to increasingly dynamic workloads, requiring efficient dynamic resource allocation strategies to satisfy hard or soft real-time constraints. This article provides an extensive survey of hard and soft real-time dynamic resource allocation strategies proposed since the mid-1990s and highlights the emerging trends for multi-/many-core systems. The survey covers a taxonomy of the resource allocation strategies and considers their various optimization objectives, which have been used to provide comprehensive comparison. The strategies employ various principles, such as market and biological concepts, to perform the optimizations. The trend followed by the resource allocation strategies, open research challenges, and likely emerging research directions have also been provided

Task Partitioning and Mapping Algorithms for Multi-core Packet Processing Systems

In this work, we have explored different task mapping algorithms for multi-core, packet processing systems. we also implemented these algorithms and compared the results of the algorithms. We first reviewed the previously designed algorithms which include UDFS algorithm and duplication process. We then applied the KL algorithm to our problem and were able to reduce the inter-processor communication by 20% while maintaining the similar utilization. We then modified the original KL algorithm by considering utilization during the mapping process. In this extended KL algorithm, we incorporated a tradeoff factor alpha to tradeoff between inter-processor communication and processor utilization. The best alpha is different for different system configurations in terms of communication bandwidth and computing power. Simulated annealing(SA) algorithm was then implemented. The parameters for SA algorithm were decided by following literature or by doing experiments. Results from SA algorithm shows that it can produce decent results that are comparable to KL algorithm. In order to further improve the utilization, merging operation was applied to the task graph before mapping algorithms were applied. The mapping results showed that merging is a good way to improve the utilization and at the same time keep the communication cost lower. Finally, we applied the mapping algorithms to different packet processing system architectures. The results show how inter-processor communication cost and processor utilization change as system architecture changes

Intelligent Management of Mobile Systems through Computational Self-Awareness

Runtime resource management for many-core systems is increasingly complex. The complexity can be due to diverse workload characteristics with conflicting demands, or limited shared resources such as memory bandwidth and power. Resource management strategies for many-core systems must distribute shared resource(s) appropriately across workloads, while coordinating the high-level system goals at runtime in a scalable and robust manner. To address the complexity of dynamic resource management in many-core systems, state-of-the-art techniques that use heuristics have been proposed. These methods lack the formalism in providing robustness against unexpected runtime behavior. One of the common solutions for this problem is to deploy classical control approaches with bounds and formal guarantees. Traditional control theoretic methods lack the ability to adapt to (1) changing goals at runtime (i.e., self-adaptivity), and (2) changing dynamics of the modeled system (i.e., self-optimization). In this chapter, we explore adaptive resource management techniques that provide self-optimization and self-adaptivity by employing principles of computational self-awareness, specifically reflection. By supporting these self-awareness properties, the system can reason about the actions it takes by considering the significance of competing objectives, user requirements, and operating conditions while executing unpredictable workloads