Parallel Performance

Our multipole code D-PMTA, the Distributed Parallel Multipole Tree Algorithm, is a message passing code which runs both on workstation clusters and on tightly coupled machines such as the Cray T3D/T3E [11]. Figure 3 shows the parallel performance of D-PMTA on a moderately large simulation on the Cray T3E the scalability is not affected by adding the macroscopic option. 

NAMD 2 added several new design goals. First, parallel performance needed to be increased through more parallelism and better load balancing. Second, communication efficiency needed to be improved without adding application-... 

The observation that certain kinds of parallel-computing architectures best support only certain kinds of problems seems to be general. The further observation that interprocessor communication can be the primary impediment to parallel performance is also general. As of this writing, any hope of a truly general purpose parallel computer seems to be remote. The best hope may He in software efforts that describe problems at higher levels of abstraction, which can then be ported and optimized for different parallel architectures (22). 

Analysis of the sample quality either from synthesis approaches or from natural sources is usually performed by techniques like HPLC and capillary electrophoresis (CE) coupled to UV/VIS-, MS-, IR-, or NMR detection. Because the working procedures of these instruments are more or less serial, there exist an enormous need in new technologies allowing parallel performances and further miniaturization. 

In this study, the automatic network designer was utilized for 10 parallel runs incorporating the same performance parameters as above. All of the 10 parallel performances proposed a 14 1 1 architecture. Each of the MLP ANN named modeling network in this work performed pattern recognition analysis with a 100% accuracy rate. In order to confirm the pattern recognition ability and the robustness of the proposed MLP ANN model, leave-one-out cross validation (40) was also carried out (i.e., the sample to be classified was deleted from the data set for the training of MLP ANN). The MLP ANNs... 

In the case of chemical and physico-chemical tests, unless otherwise specified, the technical inspection must start with at least two measured amounts, or two mutually corroborating determinations, in other tests, it is necessary to start with at least two parallel performed control determinations. 

This is a crucial requirement with ANCOVA, which sometimes cannot be met. If the treatment slopes are not parallel— that is, they interact—do not use ANCOVA. To test for parallelism, perform a parallelism test or plot out the covariates to determine this. If the slopes are not parallel, perform separate... 

In this chapter we will consider issues pertaining to parallel performance modeling. We first introduce some network performance characteristics for parallel computers that must be considered when modeling parallel performance. We then present several performance measures for parallel programs, and we discuss how to develop a performance model for a parallel algorithm. Finally, we will discuss how to evaluate performance data and illustrate how reported performance data can be potentially misleading. 

A number of ways to report misleading parallel performance data have been discussed elsewhere/ including how to boost performance data by comparing with code that is nonoptimal in a number of ways. Performance data are most often presented in the form of speedup curves, and it is therefore important to ascertain that the presented speedups are, in fact, representative of the typical parallel performance of the algorithm. Below we will discuss a couple of commonly encoimtered practices for presenting speedups that can lead to misrepresentation of performance data. 

We have used expressions involving the latency, a, and inverse bandwidth, /3, to model the communication time. An alternative model, the Hockney model, is sometimes used for the communication time in a parallel algorithm. The Hockney model expresses the time required to send a message between two processes in terms of the parameters Too and ni, which represent the asymptotic bandwidth and the message length for which half of the asymptotic bandwidth is attained, respectively. Metrics other than the speedup and efficiency are used in parallel computing. One such metric is the Karp-Flatt metric, also referred to as the experimentally determined serial fraction. This metric is intended to be used in addition to the speedup and efficiency, and it is easily computed. The Karp-Flatt metric can provide information on parallel performance characteristics that caimot be obtained from the speedup and efficiency, for instance, whether degrading parallel performance is caused by incomplete parallelization or by other factors such as load imbalance and communication overhead. ... 

To analyze the performance of algorithms (a) and (b), we will first obtain expressions for the parallel efficiencies. For these algorithms, which are fully parallelized and involve no communication, load imbalance is the only factor that may contribute significantly to lowering the parallel efficiency, and to predict the parallel performance, we must be able to estimate this load imbalance. Additionally, to make quantitative predictions for the efficiency, it is necessary to collect some statistics for the computational times required for evaluation of the integrals in a shell quartet. 

To employ the performance models in Eqs. 7.7 and 7.8 for quantitive predictions of the parallel performance, we need an expression for k p) as well as the means and standard deviations associated with the integral computation. We will discuss below how to obtain these parameters, and we will illustrate both the predicted and the actual, measured performance for the two algorithms. 

Processes request tasks (atom quartets) by calling the function get quartet, which has been implemented in both a dynamic and a static version. The dynamic work distribution uses a manager-worker model with a manager process dedicated to distributing tasks to the other processes, whereas the static version employs a round-robin distribution of tasks. When the number of processes is small, fhe sfafic scheme achieves the best parallel performance because the dynamic scheme, when run on p processes, uses only p - 1 processes for compulation. As the number of processes increases, however, the parallel performance for the dynamic task distribution surpasses that of the static scheme, whose efficiency is reduced by load imbalance. Wifh fhe entire Fock and density matrix available to every process, no communication is required during the computation of the Fock matrix other than the fetching of tasks in the dynamic scheme. After all ABCD tasks have been processed, a global summation is required to add the contributions to the Fock matrix from all processes and send the result to every process. 

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