Bounding Cache Miss Costs of Multithreaded Computations Under General Schedulers

We analyze the caching overhead incurred by a class of multithreaded algorithms when scheduled by an arbitrary scheduler. We obtain bounds that match or improve upon the well-known $O(Q+S \cdot (M/B))$ caching cost for the randomized work stealing (RWS) scheduler, where $S$ is the number of steals, $Q$ is the sequential caching cost, and $M$ and $B$ are the cache size and block (or cache line) size respectively.Comment: Extended abstract in Proceedings of ACM Symp. on Parallel Alg. and Architectures (SPAA) 2017, pp. 339-350. This revision has a few small updates including a missing citation and the replacement of some big Oh terms with precise constant

Configurable Strategies for Work-stealing

Work-stealing systems are typically oblivious to the nature of the tasks they are scheduling. For instance, they do not know or take into account how long a task will take to execute or how many subtasks it will spawn. Moreover, the actual task execution order is typically determined by the underlying task storage data structure, and cannot be changed. There are thus possibilities for optimizing task parallel executions by providing information on specific tasks and their preferred execution order to the scheduling system. We introduce scheduling strategies to enable applications to dynamically provide hints to the task-scheduling system on the nature of specific tasks. Scheduling strategies can be used to independently control both local task execution order as well as steal order. In contrast to conventional scheduling policies that are normally global in scope, strategies allow the scheduler to apply optimizations on individual tasks. This flexibility greatly improves composability as it allows the scheduler to apply different, specific scheduling choices for different parts of applications simultaneously. We present a number of benchmarks that highlight diverse, beneficial effects that can be achieved with scheduling strategies. Some benchmarks (branch-and-bound, single-source shortest path) show that prioritization of tasks can reduce the total amount of work compared to standard work-stealing execution order. For other benchmarks (triangle strip generation) qualitatively better results can be achieved in shorter time. Other optimizations, such as dynamic merging of tasks or stealing of half the work, instead of half the tasks, are also shown to improve performance. Composability is demonstrated by examples that combine different strategies, both within the same kernel (prefix sum) as well as when scheduling multiple kernels (prefix sum and unbalanced tree search)

An efficient multi-core implementation of a novel HSS-structured multifrontal solver using randomized sampling

We present a sparse linear system solver that is based on a multifrontal variant of Gaussian elimination, and exploits low-rank approximation of the resulting dense frontal matrices. We use hierarchically semiseparable (HSS) matrices, which have low-rank off-diagonal blocks, to approximate the frontal matrices. For HSS matrix construction, a randomized sampling algorithm is used together with interpolative decompositions. The combination of the randomized compression with a fast ULV HSS factorization leads to a solver with lower computational complexity than the standard multifrontal method for many applications, resulting in speedups up to 7 fold for problems in our test suite. The implementation targets many-core systems by using task parallelism with dynamic runtime scheduling. Numerical experiments show performance improvements over state-of-the-art sparse direct solvers. The implementation achieves high performance and good scalability on a range of modern shared memory parallel systems, including the Intel Xeon Phi (MIC). The code is part of a software package called STRUMPACK -- STRUctured Matrices PACKage, which also has a distributed memory component for dense rank-structured matrices

Implementation of the Low-Cost Work Stealing Algorithm for parallel computations

For quite a while, CPU’s clock speed has stagnated while the number of cores keeps increasing. Because of this, parallel computing rose as a paradigm for programming on multi-core architectures, making it critical to control the costs of communication. Achieving this is hard, creating the need for tools that facilitate this task. Work Stealing (WSteal) became a popular option for scheduling multithreaded com- putations. It ensures scalability and can achieve high performance by spreading work across processors. Each processor owns a double-ended queue where it stores its work. When such deque is empty, the processor becomes a thief, attempting to steal work, at random, from other processors’ deques. This strategy was proved to be efficient and is still currently used in state-of-the-art WSteal algorithms. However, due to the concur- rent nature of the deque, local operations require expensive memory fences to ensure correctness. This means that even when a processor is not stealing work from others, it still incurs excessive overhead due to the local accesses to the deque. Moreover, the pure receiver-initiated approach to load balancing, as well as, the randomness of the targeting of a victim makes it not suitable for scheduling computations with few or unbalanced parallelism. In this thesis, we explore the various limitations of WSteal in addition to solutions proposed by related work. This is necessary to help decide on possible optimizations for the Low-Cost Work Stealing (LCWS) algorithm, proposed by Paulino and Rito, that we implemented in C++. This algorithm is proven to have exponentially less overhead than the state-of-the-art WSteal algorithms. Such implementation will be tested against the canonical WSteal and other variants that we implemented so that we can quantify the gains of the algorithm.Já faz algum tempo desde que a velocidade dos CPUs tem vindo a estagnar enquanto o número de cores tem vindo a subir. Por causa disto, o ramo de computação paralela subiu como paradigma para programação em arquiteturas multi-core, tornando crítico controlar os custos associados de comunicação. No entanto, isto não é uma tarefa fácil, criando a necessidade de criar ferramentas que facilitem este controlo. Work Stealing (WSteal) tornou-se uma opção popular para o escalonamento de com- putações concorrentes. Este garante escalabilidade e consegue alcançar alto desempenho por distribuir o trabalho por vários processadores. Cada processador possui uma fila du- plamente terminada (deque) onde é guardado o trabalho. Quando este deque está vazio, o processador torna-se um ladrão, tentando roubar trabalho do deque de um outro pro- cessador, escolhido aleatoriamente. Esta estratégia foi provada como eficiente e ainda é atualmente usada em vários algoritmos WSteal. Contudo, devido à natureza concorrente do deque, operações locais requerem barreiras de memória, cujo correto funcionamento tem um alto custo associado. Além disso, a estratégia pura receiver-initiated (iniciada pelo recetor) de balanceamento de carga, assim como a aleatoriedade no processo de es- colha de uma vitima faz com que o algoritmo não seja adequado para o scheduling de computações com pouco ou desequilibrado paralelismo. Nesta tese, nós exploramos as várias limitações de WSteal, para além das soluções propostas por trabalhos relacionados. Isto é um passo necessário para ajudar a decidir possíveis otimisações para o algoritmo Low-Cost Work Stealing (LCWS), proposto por Paulino e Rito, que implementámos em C++. Este algoritmo está provado como tendo exponencialmente menos overhead que outros algoritmos de WSteal. Tal implementação será testada e comparada com o algoritmo canónico de WSteal, assim como outras suas variantes que implementámos para que possamos quantificar os ganhos do algoritmo

Hybrid static/dynamic scheduling for already optimized dense matrix factorization

We present the use of a hybrid static/dynamic scheduling strategy of the task dependency graph for direct methods used in dense numerical linear algebra. This strategy provides a balance of data locality, load balance, and low dequeue overhead. We show that the usage of this scheduling in communication avoiding dense factorization leads to significant performance gains. On a 48 core AMD Opteron NUMA machine, our experiments show that we can achieve up to 64% improvement over a version of CALU that uses fully dynamic scheduling, and up to 30% improvement over the version of CALU that uses fully static scheduling. On a 16-core Intel Xeon machine, our hybrid static/dynamic scheduling approach is up to 8% faster than the version of CALU that uses a fully static scheduling or fully dynamic scheduling. Our algorithm leads to speedups over the corresponding routines for computing LU factorization in well known libraries. On the 48 core AMD NUMA machine, our best implementation is up to 110% faster than MKL, while on the 16 core Intel Xeon machine, it is up to 82% faster than MKL. Our approach also shows significant speedups compared with PLASMA on both of these systems

A Review on Preventing Insider Threats and Stealthy Attacks from Sonet Site

Online social networks (OSNs) give another measurement to individuals' lives by bringing forth online social orders. OSNs have upset the human experience, however they have likewise made a stage for gatecrashers to disperse diseases and direct cybercrime. An OSN gives an entrepreneurial assault stage to cybercriminals through which they can spread contaminations at a huge scale. Assailants perform unapproved and malevolent exercises on OSN. Assaults can be an executable document, an expansion, an adventure code, and so on., that behaviors malignant tasks in OSNs with genuine effect on clients. Moreover, Intruders influence OSNs with different intensions, for example, to take basic information and adapt it for monetary profits. Insider dangers have turned into a genuine worry for some associations today. A model for OSN is to introduced to avoid insider danger misuses and to protect the classification. Multilevel security instrument is connected amid the enlistment and login level. At enlistment organize one time randomized alphanumeric watchword will be created and send to the clients by means of email though at login arrange randomized graphical secret word will be connected to counteract non malignant movement