Parallel Disk Model Research Articles

Contact Potential Difference of Au/TiO<sub>2</sub>(110) Model Catalysts Measured by Kelvin Probe Force Microscopy

Contact potential difference (CPD) of Au/TiO2 model catalysts was measured by Kelvin probe force microscopy (KFM). The CPD of Au particles was lower than that on the TiO2(110) surface and the difference of CPD between them increased with the particle height. By applying the parallel disk model, the work function difference and the charge density at the Au/TiO2 interface were estimated to be 0.19 eV and 0.01 C/m2, respectively. We have previously measured local barrier height (LBH) of Au/TiO2 by scanning tunneling microscopy. From the results of CPD and LBH measurements, we found a possibility that the localized charge at the Au/TiO2 interface plays some role in the catalytic activity of Au catalysts. [DOI: 10.1380/ejssnt.2012.59]

Distribution sort with randomized cycling

Parallel independent disks can enhance the performance of external memory (EM) algorithms, but the programming task is often difficult. Each disk can service only one read or write request at a time; the challenge is to keep the disks as busy as possible. In this article, we develop a randomized allocation discipline for parallel independent disks, called randomized cycling . We show how it can be used as the basis for an efficient distribution sort algorithm, which we call randomized cycling distribution sort (RCD). We prove that the expected I/O complexity of RCD is optimal. The analysis uses a novel reduction to a scenario with significantly fewer probabilistic interdependencies. We demonstrate RCD's practicality by experimental simulations. Using the randomized cycling discipline, algorithms developed for the unrealistic multihead disk model can be simulated on the realistic parallel disk model for the class of multipass algorithms, which make a complete pass through their data before accessing any element a second time. In particular, algorithms based upon the well-known distribution and merge paradigms of EM computation can be optimally extended from a single disk to parallel disks.

Slabpose Columnsort: A New Oblivious Algorithm for Out-of-Core Sorting on Distributed-Memory Clusters

Our goal is to develop a robust out-of-core sorting program for a distributed-memory cluster. The literature contains two dominant paradigms for out-of-core sorting algorithms: merging-based and partitioning-based. We explore a third paradigm, that of oblivious algorithms. Unlike the two dominant paradigms, oblivious algorithms do not depend on the input keys and therefore lead to predetermined I/O and communication patterns in an out-of-core setting. Predetermined I/O and communication patterns facilitate overlapping I/O, communication, and computation for efficient implementation. We have developed several out-of-core sorting programs using the paradigm of oblivious algorithms. Our baseline implementation, 3-pass columnsort, was based on Leighton's columnsort algorithm. Though efficient in terms of I/O and communication, 3-pass columnsort has a restriction on the maximum problem size. As our first effort toward relaxing this restriction, we developed two implementations: subblock columnsort and M-columnsort. Both of these implementations incur substantial performance costs: subblock columnsort performs additional disk I/O, and M-columnsort needs substantial amounts of extra communication and computation. In this paper we present slabpose columnsort, a new oblivious algorithm that we have designed explicitly for the out-of-core setting. Slabpose columnsort relaxes the problem-size restriction at no extra I/O or communication cost. Experimental evidence on a Beowulf cluster shows that unlike subblock columnsort and M-columnsort, slabpose columnsort runs almost as fast as 3-pass columnsort. To the best of our knowledge, our implementations are the first out-of-core multiprocessor sorting algorithms that make no assumptions about the keys and produce output that is perfectly load balanced and in the striped order assumed by the Parallel Disk Model.

PC-OPT: optimal offline prefetching and caching for parallel I/O systems

We address the problem of prefetching and caching in a parallel I/O system and present a new algorithm for parallel disk scheduling. Traditional buffer management algorithms that minimize the number of block misses are substantially suboptimal in a parallel I/O system where multiple I/Os can proceed simultaneously. We show that in the off line case, where a priori knowledge of all the requests is available, PC-OPT performs the minimum number of I/Os to service the given I/O requests. This is the first parallel I/O scheduling algorithm that is provably offline optimal in the parallel disk model. In the online case, we study the context of global L-block lookahead, which gives the buffer management algorithm a lookahead consisting of L distinct requests. We show that the competitive ratio of PC-OPT, with global L-block lookahead, is /spl Theta/(M - L + D), when L /spl les/ M, and /spl Theta/(MD/L), when L > M, where the number of disks is D and buffer size is M.

An efficient algorithm for out-of-core matrix transposition

Efficient transposition of out-of-core matrices has been widely studied. These efforts have focused on reducing the number of I/O operations. However, in state-of-the-art architectures, the memory-memory data transfer time and the index computation time are also significant components of the overall time. In this paper, we propose an algorithm that considers the index computation time and the I/O time and reduces the overall execution time. Our algorithm reduces the total execution time by reducing the number of I/O operations and eliminating the index computation. In doing so, two techniques are employed: writing the data on to disk in pre-defined patterns and balancing the number of disk read and write operations. The index computation time, which is an expensive operation involving two divisions and a multiplication, is eliminated by partitioning the memory into read and write buffers. The expensive in-processor permutation is replaced by data collection from the read buffer to the write buffer. Even though this partitioning may increase the number of I/O operations for some cases, it results in an overall reduction in the execution time due to the elimination of the expensive index computation. Our algorithm is analyzed using the well-known linear model and the parallel disk model. The experimental results on a Sun Enterprise, an SGI R12000 and a Pentium III show that our algorithm reduces the overall execution time by up to 50% compared with the best known algorithms in the literature.

Performing out-of-core FFTs on parallel disk systems

The Fast Fourier Transform (FFT) plays a key role in many areas of computational science and engineering. Although most one-dimensional FFT problems can be solved entirely in main memory, some important classes of applications require out-of-core techniques. For these, use of parallel I/O systems can improve performance considerably. This paper shows how to perform one-dimensional FFTs using a parallel disk system with independent disk accesses. We present both analytical and experimental results for performing out-of-core FFTs in two ways: using traditional virtual memory with demand paging, and using a provably asymptotically optimal algorithm for the Parallel Disk Model (PDM) of Vitter and Shriver. When run on a DEC 2100 server with a large memory and eight parallel disks, the optimal algorithm for the PDM runs up to 144.7 times faster than in-core methods under demand paging. Moreover, even including I/O costs, the normalized times for the optimal PDM algorithm are competitive, or better than, those for in-core methods even when they run entirely in memory.

Asymptotically Tight Bounds for Performing BMMC Permutations on Parallel Disk Systems

This paper presents asymptotically equal lower and upper bounds for the number of parallel I/O operations required to perform bit-matrix-multiply/complement (BMMC) permutations on the Parallel Disk Model proposed by Vitter and Shriver. A BMMC permutation maps a source index to a target index by an affine transformation over GF(2), where the source and target indices are treated as bit vectors. The class of BMMC permutations includes many common permutations, such as matrix transposition (when dimensions are powers of 2), bit-reversal permutations, vector-reversal permutations, hypercube permutations, matrix reblocking, Gray-code permutations, and inverse Gray-code permutations. The upper bound improves upon the asymptotic bound in the previous best known BMMC algorithm and upon the constant factor in the previous best known bit-permute/complement (BPC) permutation algorithm. The algorithm achieving the upper bound uses basic linear-algebra techniques to factor the characteristic matrix for the BMMC permutation into a product of factors, each of which characterizes a permutation that can be performed in one pass over the data. The factoring uses new subclasses of BMMC permutations: memoryload-dispersal (MLD) permutations and their inverses. These subclasses extend the catalog of one-pass permutations. Although many BMMC permutations of practical interest fall into subclasses that might be explicitly invoked within the source code, this paper shows how to quickly detect whether a given vector of target addresses specifies a BMMC permutation. Thus, one can determine efficiently at run time whether a permutation to be performed is BMMC and then avoid the general-permutation algorithm and save parallel I/Os by using the BMMC permutation algorithm herein.

Out-of-core FFTs with parallel disks

We examine approaches to computing the Fast Fourier Transform (FFT) when the data size exceeds the size of main memory. Analytical and experimental evidence shows that relying on native virtual memory with demand paging can yield extremely poor performance. We then present approaches based on minimizing I/O costs with the Parallel Disk Model (PDM). Each of these approaches explicitly plans and performs disk accesses so as to minimize their number.

Early experiences in evaluating the parallel disk model with the ViC ∗ implementation

Although several algorithms have been developed for the Parallel Disk Model (PDM), few have been implemented. Consequently, little has been known about the accuracy of the PDM in measuring I/O time and total running time to perform an out-of-core computation. This paper analyzes timing results on multiple-disk platforms for two PDM algorithms, out-of-core radix sort and BMMC permutations, to determine the strengths and weaknesses of the PDM. The results indicate the following. First, good PDM algorithms are usually not I/O bound. Second, of the four PDM parameters, one (problem size) is a good indicator of I/O time and running time, one (memory size) is a good indicator of I/O time but not necessarily running time, and the other two (block size and number of disks) do not necessarily indicate either I/O or running time. Third, because PDM algorithms tend not to be I/O bound, using asynchronous I/O can reduce I/O wait times significantly. The software interface to the PDM is part of the ViC ∗ run-time library. The interface is a set of wrappers that are designed to be both efficient and portable across several underlying file systems and target machines.