The "MIND" Scalable PIM Architecture

MIND (Memory, Intelligence, and Network Device) is an advanced parallel computer architecture for high performance computing and scalable embedded processing. It is a Processor-in-Memory (PIM) architecture integrating both DRAM bit cells and CMOS logic devices on the same silicon die. MIND is multicore with multiple memory/processor nodes on each chip and supports global shared memory across systems of MIND components. MIND is distinguished from other PIM architectures in that it incorporates mechanisms for efficient support of a global parallel execution model based on the semantics of message-driven multithreaded split-transaction processing. MIND is designed to operate either in conjunction with other conventional microprocessors or in standalone arrays of like devices. It also incorporates mechanisms for fault tolerance, real time execution, and active power management. This paper describes the major elements and operational methods of the MIND architecture

Distributed Simulation of High-Level Algebraic Petri Nets

In the field of Petri nets, simulation is an essential tool to validate and evaluate models. Conventional simulation techniques, designed for their use in sequential computers, are too slow if the system to simulate is large or complex. The aim of this work is to search for techniques to accelerate simulations exploiting the parallelism available in current, commercial multicomputers, and to use these techniques to study a class of Petri nets called high-level algebraic nets. These nets exploit the rich theory of algebraic specifications for high-level Petri nets: Petri nets gain a great deal of modelling power by representing dynamically changing items as structured tokens whereas algebraic specifications turned out to be an adequate and flexible instrument for handling structured items. In this work we focus on ECATNets (Extended Concurrent Algebraic Term Nets) whose most distinctive feature is their semantics which is defined in terms of rewriting logic. Nevertheless, ECATNets have two drawbacks: the occultation of the aspect of time and a bad exploitation of the parallelism inherent in the models. Three distributed simulation techniques have been considered: asynchronous conservative, asynchronous optimistic and synchronous. These algorithms have been implemented in a multicomputer environment: a network of workstations. The influence that factors such as the characteristics of the simulated models, the organisation of the simulators and the characteristics of the target multicomputer have in the performance of the simulations have been measured and characterised. It is concluded that synchronous distributed simulation techniques are not suitable for the considered kind of models, although they may provide good performance in other environments. Conservative and optimistic distributed simulation techniques perform well, specially if the model to simulate is complex or large - precisely the worst case for traditional, sequential simulators. This way, studies previously considered as unrealisable, due to their exceedingly high computational cost, can be performed in reasonable times. Additionally, the spectrum of possibilities of using multicomputers can be broadened to execute more than numeric applications

Performance measurement and analysis of PC based cluster server using SET of Architecture and modeling a scalable High performance cluster

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Spatial parallelism in the routers of asynchronous on-chip networks

State-of-the-art multi-processor systems-on-chip use on-chip networks as their communication fabric. Although most on-chip networks are implemented synchronously, asynchronous on-chip networks have several advantages over their synchronous counterparts. Timing division multiplexing (TDM) flow control methods have been utilized in asynchronous on-chip networks extensively. The synchronization required by TDM leads to significant speed penalties. Compared with using TDM methods, spatial parallelism methods, such as the spatial division multiplexing (SDM) flow control method, achieve better network throughput with less area overhead.This thesis proposes several techniques to increase spatial parallelism in the routers of asynchronous on-chip networks.Channel slicing is a new pipeline structure that alleviates the speed penalty by removing the synchronization among bit-level data pipelines. It is also found out that the lookahead pipeline using early evaluated acknowledgement can be used in routers to further improve speed.SDM is a new flow control method proposed for asynchronous on-chip networks. It improves network throughput without introducing synchronization among buffers of different frames, which is required by TDM methods. It is also found that the area overhead of SDM is smaller than the virtual channel (VC) flow control method -- the most used TDM method. The major design problem of SDM is the area consuming crossbars. A novel 2-stage Clos switch structure is proposed to replace the crossbar in SDM routers, which significantly reduces the area overhead. This Clos switch is dynamically reconfigured by a new asynchronous Clos scheduler.Several asynchronous SDM routers are implemented using these new techniques. An asynchronous VC router is also reproduced for comparison. Performance analyses show that the SDM routers outperform the VC router in throughput, area overhead and energy efficiency.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Spatial parallelism in the routers of asynchronous on-chip networks

State-of-the-art multi-processor systems-on-chip use on-chip networks as their communication fabric. Although most on-chip networks are implemented synchronously, asynchronous on-chip networks have several advantages over their synchronous counterparts. Timing division multiplexing (TDM) flow control methods have been utilized in asynchronous on-chip networks extensively. The synchronization required by TDM leads to significant speed penalties. Compared with using TDM methods, spatial parallelism methods, such as the spatial division multiplexing (SDM) flow control method, achieve better network throughput with less area overhead.This thesis proposes several techniques to increase spatial parallelism in the routers of asynchronous on-chip networks.Channel slicing is a new pipeline structure that alleviates the speed penalty by removing the synchronization among bit-level data pipelines. It is also found out that the lookahead pipeline using early evaluated acknowledgement can be used in routers to further improve speed.SDM is a new flow control method proposed for asynchronous on-chip networks. It improves network throughput without introducing synchronization among buffers of different frames, which is required by TDM methods. It is also found that the area overhead of SDM is smaller than the virtual channel (VC) flow control method -- the most used TDM method. The major design problem of SDM is the area consuming crossbars. A novel 2-stage Clos switch structure is proposed to replace the crossbar in SDM routers, which significantly reduces the area overhead. This Clos switch is dynamically reconfigured by a new asynchronous Clos scheduler.Several asynchronous SDM routers are implemented using these new techniques. An asynchronous VC router is also reproduced for comparison. Performance analyses show that the SDM routers outperform the VC router in throughput, area overhead and energy efficiency.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Modeling and optimization of high-performance many-core systems for energy-efficient and reliable computing

Thesis (Ph.D.)--Boston UniversityMany-core systems, ranging from small-scale many-core processors to large-scale high performance computing (HPC) data centers, have become the main trend in computing system design owing to their potential to deliver higher throughput per watt. However, power densities and temperatures increase following the growth in the performance capacity, and bring major challenges in energy efficiency, cooling costs, and reliability. These challenges require a joint assessment of performance, power, and temperature tradeoffs as well as the design of runtime optimization techniques that monitor and manage the interplay among them. This thesis proposes novel modeling and runtime management techniques that evaluate and optimize the performance, energy, and reliability of many-core systems. We first address the energy and thermal challenges in 3D-stacked many-core processors. 3D processors with stacked DRAM have the potential to dramatically improve performance owing to lower memory access latency and higher bandwidth. However, the performance increase may cause 3D systems to exceed the power budgets or create thermal hot spots. In order to provide an accurate analysis and enable the design of efficient management policies, this thesis introduces a simulation framework to jointly analyze performance, power, and temperature for 3D systems. We then propose a runtime optimization policy that maximizes the system performance by characterizing the application behavior and predicting the operating points that satisfy the power and thermal constraints. Our policy reduces the energy-delay product (EDP) by up to 61.9% compared to existing strategies. Performance, cooling energy, and reliability are also critical aspects in HPC data centers. In addition to causing reliability degradation, high temperatures increase the required cooling energy. Communication cost, on the other hand, has a significant impact on system performance in HPC data centers. This thesis proposes a topology-aware technique that maximizes system reliability by selecting between workload clustering and balancing. Our policy improves the system reliability by up to 123.3% compared to existing temperature balancing approaches. We also introduce a job allocation methodology to simultaneously optimize the communication cost and the cooling energy in a data center. Our policy reduces the cooling cost by 40% compared to cooling-aware and performance-aware policies, while achieving comparable performance to performance-aware policy

Parallel and Distributed Computing

The 14 chapters presented in this book cover a wide variety of representative works ranging from hardware design to application development. Particularly, the topics that are addressed are programmable and reconfigurable devices and systems, dependability of GPUs (General Purpose Units), network topologies, cache coherence protocols, resource allocation, scheduling algorithms, peertopeer networks, largescale network simulation, and parallel routines and algorithms. In this way, the articles included in this book constitute an excellent reference for engineers and researchers who have particular interests in each of these topics in parallel and distributed computing