Design and evaluation of a Thread-Level Speculation runtime library

En los próximos años es más que probable que máquinas con cientos o incluso miles de procesadores sean algo habitual. Para aprovechar estas máquinas, y debido a la dificultad de programar de forma paralela, sería deseable disponer de sistemas de compilación o ejecución que extraigan todo el paralelismo posible de las aplicaciones existentes. Así en los últimos tiempos se han propuesto multitud de técnicas paralelas. Sin embargo, la mayoría de ellas se centran en códigos simples, es decir, sin dependencias entre sus instrucciones. La paralelización especulativa surge como una solución para estos códigos complejos, posibilitando la ejecución de cualquier tipo de códigos, con o sin dependencias. Esta técnica asume de forma optimista que la ejecución paralela de cualquier tipo de código no de lugar a errores y, por lo tanto, necesitan de un mecanismo que detecte cualquier tipo de colisión. Para ello, constan de un monitor responsable que comprueba constantemente que la ejecución no sea errónea, asegurando que los resultados obtenidos de forma paralela sean similares a los de cualquier ejecución secuencial. En caso de que la ejecución fuese errónea los threads se detendrían y reiniciarían su ejecución para asegurar que la ejecución sigue la semántica secuencial. Nuestra contribución en este campo incluye (1) una nueva librería de ejecución especulativa fácil de utilizar; (2) nuevas propuestas que permiten reducir de forma significativa el número de accesos requeridos en las peraciones especulativas, así como consejos para reducir la memoria a utilizar; (3) propuestas para mejorar los métodos de scheduling centradas en la gestión dinámica de los bloques de iteraciones utilizados en las ejecuciones especulativas; (4) una solución híbrida que utiliza memoria transaccional para implementar las secciones críticas de una librería de paralelización especulativa; y (5) un análisis de las técnicas especulativas en uno de los dispositivos más vanguardistas del momento, los coprocesadores Intel Xeon Phi. Como hemos podido comprobar, la paralelización especulativa es un campo de investigación activo. Nuestros resultados demuestran que esta técnica permite obtener mejoras de rendimiento en un gran número de aplicaciones. Así, esperamos que este trabajo contribuya a facilitar el uso de soluciones especulativas en compiladores comerciales y/o modelos de programación paralela de memoria compartida.Departamento de Informática (Arquitectura y Tecnología de Computadores, Ciencias de la Computación e Inteligencia Artificial, Lenguajes y Sistemas Informáticos

Doctor of Philosophy

dissertationSolutions to Partial Di erential Equations (PDEs) are often computed by discretizing the domain into a collection of computational elements referred to as a mesh. This solution is an approximation with an error that decreases as the mesh spacing decreases. However, decreasing the mesh spacing also increases the computational requirements. Adaptive mesh re nement (AMR) attempts to reduce the error while limiting the increase in computational requirements by re ning the mesh locally in regions of the domain that have large error while maintaining a coarse mesh in other portions of the domain. This approach often provides a solution that is as accurate as that obtained from a much larger xed mesh simulation, thus saving on both computational time and memory. However, historically, these AMR operations often limit the overall scalability of the application. Adapting the mesh at runtime necessitates scalable regridding and load balancing algorithms. This dissertation analyzes the performance bottlenecks for a widely used regridding algorithm and presents two new algorithms which exhibit ideal scalability. In addition, a scalable space- lling curve generation algorithm for dynamic load balancing is also presented. The performance of these algorithms is analyzed by determining their theoretical complexity, deriving performance models, and comparing the observed performance to those performance models. The models are then used to predict performance on larger numbers of processors. This analysis demonstrates the necessity of these algorithms at larger numbers of processors. This dissertation also investigates methods to more accurately predict workloads based on measurements taken at runtime. While the methods used are not new, the application of these methods to the load balancing process is. These methods are shown to be highly accurate and able to predict the workload within 3% error. By improving the accuracy of these estimations, the load imbalance of the simulation can be reduced, thereby increasing the overall performance

Faculty Publications and Creative Works 2004

Faculty Publications & Creative Works is an annual compendium of scholarly and creative activities of University of New Mexico faculty during the noted calendar year. Published by the Office of the Vice President for Research and Economic Development, it serves to illustrate the robust and active intellectual pursuits conducted by the faculty in support of teaching and research at UNM

Parallel and External High Quality Graph Partitioning

Partitioning graphs into k blocks of roughly equal size such that few edges run between the blocks is a key tool for processing and analyzing large complex real-world networks. The graph partitioning problem has multiple practical applications in parallel and distributed computations, data storage, image processing, VLSI physical design and many more. Furthermore, recently, size, variety, and structural complexity of real-world networks has grown dramatically. Therefore, there is a demand for efficient graph partitioning algorithms that fully utilize computational power and memory capacity of modern machines. A popular and successful heuristic to compute a high-quality partitions of large networks in reasonable time is $\textit{multi-level graph partitioning}$ approach which contracts the graph preserving its structure and then partitions it using a complex graph partitioning algorithm. Specifically, the multi-level graph partitioning approach consists of three main phases: coarsening, initial partitioning, and uncoarsening. During the coarsening phase, the graph is recursively contracted preserving its structure and properties until it is small enough to compute its initial partition during the initial partitioning phase. Afterwards, during the uncoarsening phase the partition of the contracted graph is projected onto the original graph and refined using, for example, local search. Most of the research on heuristical graph partitioning focuses on sequential algorithms or parallel algorithms in the distributed memory model. Unfortunately, previous approaches to graph partitioning are not able to process large networks and rarely take in into account several aspects of modern computational machines. Specifically, the amount of cores per chip grows each year as well as the price of RAM reduces slower than the real-world graphs grow. Since HDDs and SSDs are 50 – 400 times cheaper than RAM, external memory makes it possible to process large real-world graphs for a reasonable price. Therefore, in order to better utilize contemporary computational machines, we develop efficient $\textit{multi-level graph partitioning}$ algorithms for the shared-memory and the external memory models. First, we present an approach to shared-memory parallel multi-level graph partitioning that guarantees balanced solutions, shows high speed-ups for a variety of large graphs and yields very good quality independently of the number of cores used. Important ingredients include parallel label propagation for both coarsening and uncoarsening, parallel initial partitioning, a simple yet effective approach to parallel localized local search, and fast locality preserving hash tables that effectively utilizes caches. The main idea of the parallel localized local search is that each processors refines only a small area around a random vertex reducing interactions between processors. For example, on 79 cores, our algorithms partitions a graph with more than 3 billions of edges into 16 blocks cutting 4.5% less edges than the closest competitor and being more than two times faster. Furthermore, another competitors is not able to partition this graph. We then present an approach to external memory graph partitioning that is able to partition large graphs that do not fit into RAM. Specifically, we consider the semi-external and the external memory model. In both models a data structure of size proportional to the number of edges does not fit into the RAM. The difference is that the former model assumes that a data structure of size proportional to the number of vertices fits into the RAM whereas the latter assumes the opposite. We address the graph partitioning problem in both models by adapting the size-constrained label propagation technique for the semi-external model and by developing a size-constrained clustering algorithm based on graph coloring in the external memory. Our semi-external size-constrained label propagation algorithm (or external memory clustering algorithm) can be used to compute graph clusterings and is a prerequisite for the (semi-)external graph partitioning algorithm. The algorithms are then used for both the coarsening and the uncoarsening phase of a multi-level algorithm to compute graph partitions. Our (semi-)external algorithm is able to partition and cluster huge complex networks with billions of edges on cheap commodity machines. Experiments demonstrate that the semi-external graph partitioning algorithm is scalable and can compute high quality partitions in time that is comparable to the running time of an efficient internal memory implementation. A parallelization of the algorithm in the semi-external model further reduces running times. Additionally, we develop a speed-up technique for the hypergraph partitioning algorithms. Hypergraphs are an extension of graphs that allow a single edge to connect more than two vertices. Therefore, they describe models and processes more accurately additionally allowing more possibilities for improvement. Most multi-level hypergraph partitioning algorithms perform some computations on vertices and their set of neighbors. Since these computations can be super-linear, they have a significant impact on the overall running time on large hypergraphs. Therefore, to further reduce the size of hyperedges, we develop a pin-sparsifier based on the min-hash technique that clusters vertices with similar neighborhood. Further, vertices that belong to the same cluster are substituted by one vertex, which is connected to their neighbors, therefore, reducing the size of the hypergraph. Our algorithm sparsifies a hypergraph such that the resulting graph can be partitioned significantly faster without loss in quality (or with insignificant loss). On average, KaHyPar with sparsifier performs partitioning about 1.5 times faster while preserving solution quality if hyperedges are large. All aforementioned frameworks are publicly available

Impact FD: An Unreliable Failure Detector Based on Process Relevance and Confidence in the System

International audienceThis paper presents a new unreliable failure detector, called the Impact failure detector (FD) that, contrarily to the majority of traditional FDs, outputs a trust level value which expresses the degree of confidence in the system. An impact factor is assigned to each process and the trust level is equal to the sum of the impact factors of the processes not suspected of failure. Moreover, a threshold parameter defines a lower bound value for the trust level, over which the confidence in the system is ensured. In particular, we defined a f l exi bi l i t y property that denotes the capacity of the Impact FD to tolerate a certain margin of failures or false suspicions, i.e., its capacity of considering different sets of responses that lead the system to trusted states. The Impact FD is suitable for systems that present node redundancy, heterogeneity of nodes, clustering feature, and allow a margin of failures which does not degrade the confidence in the system. The paper also includes a timer-based distributed algorithm which implements an Impact FD, as well as its proof of correctness, for systems whose links are lossy asynchronous or for those whose all (or some) links are eventually timely. Performance evaluation results, based on PlanetLab [1] traces, confirm the degree of flexible applicability of our failure detector and that, due to the accepted margin of failure, both failures and false suspicions are more tolerated when compared to traditional unreliable failure detectors

Efficient processor allocation strategies for mesh-connected multicomputers

Abstract Efficient processor allocation and job scheduling algorithms are critical if the full computational power of large-scale multicomputers is to be harnessed effectively. Processor allocation is responsible for selecting the set of processors on which parallel jobs are executed, whereas job scheduling is responsible for determining the order in which the jobs are executed. Many processor allocation strategies have been devised for mesh-connected multicomputers and these can be divided into two main categories: contiguous and non-contiguous. In contiguous allocation, jobs are allocated distinct contiguous processor sub-meshes for the duration of their execution. Such a strategy could lead to high processor fragmentation which degrades system performance in terms of, for example, the turnaround time and system utilisation. In non-contiguous allocation, a job can execute on multiple disjoint smaller sub-meshes rather than waiting until a single sub-mesh of the requested size and shape is available. Although non-contiguous allocation increases message contention inside the network, lifting the contiguity condition can reduce processor fragmentation and increase system utilisation. Processor fragmentation can be of two types: internal and external. The former occurs when more processors are allocated to a job than it requires while the latter occurs when there are free processors enough in number to satisfy another job request, but they are not allocated to it because they are not contiguous. A lot of efforts have been devoted to reducing fragmentation, and a number of contiguous allocation strategies have been devised to recognize complete sub-meshes during allocation. Most of these strategies have been suggested for 2D mesh-connected multicomputers. However, although the 3D mesh has been the underlying network topology for a number of important multicomputers, there has been relatively little activity with regard to designing similar strategies for such a network. The very few contiguous allocation strategies suggested for the 3D mesh achieve complete sub-mesh recognition ability only at the expense of a high allocation overhead (i.e., allocation and de-allocation time). Furthermore, the allocation overhead in the existing contiguous strategies often grows with system size. The main challenge is therefore to devise an efficient contiguous allocation strategy that can exhibit good performance (e.g., a low job turnaround time and high system utilisation) with a low allocation overhead. The first part of the research presents a new contiguous allocation strategy, referred to as Turning Busy List (TBL), for 3D mesh-connected multicomputers. The TBL strategy considers only those available free sub-meshes which border from the left of those already allocated sub-meshes or which have their left boundaries aligned with that of the whole mesh network. Moreover TBL uses an efficient scheme to facilitate the detection of such available sub-meshes while maintaining a low allocation overhead. This is achieved through maintaining a list of allocated sub-meshes in order to efficiently determine the processors that can form an allocation sub-mesh for a new allocation request. The new strategy is able to identify a free sub-mesh of the requested size as long as it exists in the mesh. Results from extensive simulations under various operating loads reveal that TBL manages to deliver competitive performance (i.e., low turnaround times and high system utilisation) with a much lower allocation overhead compared to other well-known existing strategies. Most existing non-contiguous allocation strategies that have been suggested for the mesh suffer from several problems that include internal fragmentation, external fragmentation, and message contention inside the network. Furthermore, the allocation of processors to job requests is not based on free contiguous sub-meshes in these existing strategies. The second part of this research proposes a new non-contiguous allocation strategy, referred to as Greedy Available Busy List (GABL) strategy that eliminates both internal and external fragmentation and alleviates the contention in the network. GABL combines the desirable features of both contiguous and non-contiguous allocation strategies as it adopts the contiguous allocation used in our TBL strategy. Moreover, GABL is flexible enough in that it could be applied to either the 2D or 3D mesh. However, for the sake of the present study, the new non-contiguous allocation strategy is discussed for the 2D mesh and compares its performance against that of well-known non-contiguous allocation strategies suggested for this network. One of the desirable features of GABL is that it can maintain a high degree of contiguity between processors compared to the previous allocation strategies. This, in turn, decreases the number of sub-meshes allocated to a job, and thus decreases message distances, resulting in a low inter-processor communication overhead. The performance analysis here indicates that the new proposed strategy has lower turnaround time than the previous non-contiguous allocation strategies for most considered cases. Moreover, in the presence of high message contention due to heavy network traffic, GABL exhibits superior performance in terms of the turnaround time over the previous contiguous and non-contiguous allocation strategies. Furthermore, GABL exhibits a high system utilisation as it manages to eliminate both internal and external fragmentation. The performance of many allocation strategies including the ones suggested above, has been evaluated under the assumption that job execution times follow an exponential distribution. However, many measurement studies have convincingly demonstrated that the execution times of certain computational applications are best characterized by heavy-tailed job execution times; that is, many jobs have short execution times and comparatively few have very long execution times. Motivated by this observation, the final part of this thesis reviews the performance of several contiguous allocation strategies, including TBL, in the context of heavy-tailed distributions. This research is the first to analyze the performance impact of heavy-tailed job execution times on the allocation strategies suggested for mesh-connected multicomputers. The results show that the performance of the contiguous allocation strategies degrades sharply when the distribution of job execution times is heavy-tailed. Further, adopting an appropriate scheduling strategy, such as Shortest-Service-Demand (SSD) as opposed to First-Come-First-Served (FCFS), can significantly reduce the detrimental effects of heavy-tailed distributions. Finally, while the new contiguous allocation strategy (TBL) is as good as the best competitor of the previous contiguous allocation strategies in terms of job turnaround time and system utilisation, it is substantially more efficient in terms of allocation overhead

Annual Report of the University, 1999-2000, Volumes 1-4

The Robert O. Anderson School and Graduate School of Management at The University of New Mexico Period of Report: July 1, 1999 to June 30, 2000 Submitted by Howard L. Smith, Dean The Anderson Schools of Management is divided into four distinct divisions- the Department of Accounting; the Department of Finance, International and Technology Management; the Department of Marketing, Information and Decision Sciences; and the Department of Organizational Studies. This structure provides an opportunity for The Anderson Schools to develop four distinct areas of excellence, proven by results reported here. I. Significant Developments During the Academic Year The Anderson Schools of Management • As a result of the multi-year gift from the Ford Motor Company, completed renovation of The Schools\u27 Advisement and Placement Center, as well as all student organization offices. • The Ford gift also provided for $100,000 to support faculty research, case studies and course development. • The Schools revised the MBA curriculum to meet the changing needs of professional, advanced business education. • The Schools updated computer laboratory facilities, with the addition of a 45-unit cluster for teaching and student work. • The faculty and staff of The Schools furthered outreach in economic development activities by participating directly as committee members and leaders in the cluster workgroups of the Next Generation Economy Initiative. • The faculty, staff and students of The Schools contributed to the development of the Ethics in Business Awards; particularly exciting was the fact that all nominee packages were developed by student teams from The Anderson Schools. • The Schools continue to generate more credit hours per faculty member than any other division of the UNM community. The Accounting Department • Preparation and presentation of a progress report to accrediting body, the AACSB. The Department of Finance, International and Technology Management • The Department continued to focus on expansion of the Management of Technology program as a strategic strength of The Schools. The Department of Marketing. Information and Decision Sciences • Generated 9022 credit hours, with a student enrollment of 3070. The Department of Organizational Studies • Coordinated the 9th UNM Universidad de Guanajuato (UG) Mexico Student Exchange