Dynamic remapping of parallel computations with varying resource demands

A large class of computational problems is characterized by frequent synchronization, and computational requirements which change as a function of time. When such a problem must be solved on a message passing multiprocessor machine, the combination of these characteristics lead to system performance which decreases in time. Performance can be improved with periodic redistribution of computational load; however, redistribution can exact a sometimes large delay cost. We study the issue of deciding when to invoke a global load remapping mechanism. Such a decision policy must effectively weigh the costs of remapping against the performance benefits. We treat this problem by constructing two analytic models which exhibit stochastically decreasing performance. One model is quite tractable; we are able to describe the optimal remapping algorithm, and the optimal decision policy governing when to invoke that algorithm. However, computational complexity prohibits the use of the optimal remapping decision policy. We then study the performance of a general remapping policy on both analytic models. This policy attempts to minimize a statistic W(n) which measures the system degradation (including the cost of remapping) per computation step over a period of n steps. We show that as a function of time, the expected value of W(n) has at most one minimum, and that when this minimum exists it defines the optimal fixed-interval remapping policy. Our decision policy appeals to this result by remapping when it estimates that W(n) is minimized. Our performance data suggests that this policy effectively finds the natural frequency of remapping. We also use the analytic models to express the relationship between performance and remapping cost, number of processors, and the computation's stochastic activity

Statistical methodologies for the control of dynamic remapping

Following an initial mapping of a problem onto a multiprocessor machine or computer network, system performance often deteriorates with time. In order to maintain high performance, it may be necessary to remap the problem. The decision to remap must take into account measurements of performance deterioration, the cost of remapping, and the estimated benefits achieved by remapping. We examine the tradeoff between the costs and the benefits of remapping two qualitatively different kinds of problems. One problem assumes that performance deteriorates gradually, the other assumes that performance deteriorates suddenly. We consider a variety of policies for governing when to remap. In order to evaluate these policies, statistical models of problem behaviors are developed. Simulation results are presented which compare simple policies with computationally expensive optimal decision policies; these results demonstrate that for each problem type, the proposed simple policies are effective and robust

Towards developing robust algorithms for solving partial differential equations on MIMD machines

Methods for efficient computation of numerical algorithms on a wide variety of MIMD machines are proposed. These techniques reorganize the data dependency patterns to improve the processor utilization. The model problem finds the time-accurate solution to a parabolic partial differential equation discretized in space and implicitly marched forward in time. The algorithms are extensions of Jacobi and SOR. The extensions consist of iterating over a window of several timesteps, allowing efficient overlap of computation with communication. The methods increase the degree to which work can be performed while data are communicated between processors. The effect of the window size and of domain partitioning on the system performance is examined both by implementing the algorithm on a simulated multiprocessor system

Performance tradeoffs in static and dynamic load balancing strategies

The problem of uniformly distributing the load of a parallel program over a multiprocessor system was considered. A program was analyzed whose structure permits the computation of the optimal static solution. Then four strategies for load balancing were described and their performance compared. The strategies are: (1) the optimal static assignment algorithm which is guaranteed to yield the best static solution, (2) the static binary dissection method which is very fast but sub-optimal, (3) the greedy algorithm, a static fully polynomial time approximation scheme, which estimates the optimal solution to arbitrary accuracy, and (4) the predictive dynamic load balancing heuristic which uses information on the precedence relationships within the program and outperforms any of the static methods. It is also shown that the overhead incurred by the dynamic heuristic is reduced considerably if it is started off with a static assignment provided by either of the other three strategies

Implementation of a parallel unstructured Euler solver on shared and distributed memory architectures

An efficient three dimensional unstructured Euler solver is parallelized on a Cray Y-MP C90 shared memory computer and on an Intel Touchstone Delta distributed memory computer. This paper relates the experiences gained and describes the software tools and hardware used in this study. Performance comparisons between two differing architectures are made

caGrid-Enabled caBIGTM Silver Level Compatible Head and Neck Cancer Tissue Database System

There are huge amounts of biomedical data generated by research labs in each cancer institution. The data are stored in various formats and accessed through numerous interfaces. It is very difficult to exchange and integrate the data among different cancer institutions, even among different research labs within the same institution, in order to discover useful biomedical knowledge for the healthcare community. In this paper, we present the design and implementation of a caGrid-enabled caBIGTM silver level compatible head and neck cancer tissue database system. The system is implemented using a set of open source software and tools developed by the NCI, such as the caCORE SDK and caGrid. The head and neck cancer tissue database system has four interfaces: Web-based, Java API, XML utility, and Web service. The system has been shown to provide robust and programmatically accessible biomedical information services that syntactically and semantically interoperate with other resources

Эффективность капецитабина по сравнению с 5-фторурацилом при раке толстой кишки и желудка: обновленный метаанализ выживаемости в шести клинических исследованиях

Оральный фторпиримидин — капецитабин — широко изучен в сравнительных исследованиях с вводимым внутривенно 5-фторурацилом как монотерапевтическое средство или в комплексном приме- нении при метастатическом колоректальном раке (МКРР) и метастатическом раке желудка (МРЖ). По рекомендации Европейских органов здравоохранения выполнен метаанализ эффективности применения капецитабина по сравнению с 5-фторурацилом при МКРР и МРЖ

A Study of Thymidylate Synthase Expression as a Biomarker for Resectable Colon Cancer: Alliance (Cancer and Leukemia Group B) 9581 and 89803.

PurposeTumor levels of thymidylate synthase (TS), a target of 5-fluorouracil (5-FU)-based chemotherapy for colorectal cancer, have been studied as a predictive or prognostic biomarker with mixed results.Patients and methodsTumor TS levels were prospectively evaluated in two adjuvant therapy trials for patients with resected stage II or III colon cancer. TS expression was determined by standard immunohistochemistry and by automated quantitative analysis. Tumor mismatch repair deficiency (MMR-D) and BRAF c.1799T > A (p.V600E) mutation status were also examined. Relationships between tumor TS, MMR-D, and BRAF mutation status, overall survival (OS), and disease-free survival (DFS) were investigated in the subset of stage III patients.ResultsPatients whose tumors demonstrated high TS expression experienced better treatment outcomes, with DFS hazard ratio (HR) = 0.67, 95% confidence interval (CI) = 0.53, 0.84; and OS HR = 0.68, 95% CI = 0.53, 0.88, for high versus low TS expression, respectively. No significant interaction between TS expression and stage was observed (DFS: interaction HR = 0.94; OS: interaction HR = 0.94). Tumors with high TS expression were more likely to demonstrate MMR-D (22.2% vs. 12.8%; p =  .0003). Patients whose tumors demonstrated both high TS and MMR-D had a 7-year DFS of 77%, compared with 58% for those whose tumors had low TS and were non-MMR-D (log-rank p =  .0006). Tumor TS expression did not predict benefit of a particular therapeutic regimen.ConclusionThis large prospective analysis showed that high tumor TS levels were associated with improved DFS and OS following adjuvant therapy for colon cancer, although tumor TS expression did not predict benefit of 5-FU-based chemotherapy. The Oncologist 2017;22:107-114Implications for Practice: This study finds that measurement of tumor levels of thymidylate synthase is not helpful in assigning specific adjuvant treatment for colorectal cancer. It also highlights the importance of using prospective analyses within treatment clinical trials as the optimal method of determining biomarker utility

Demystifying Data Science Projects: A Look on the People and Process of Data Science Today

Peer reviewe