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8.4 Bibliographic Remarks

Matrix transposition with 1-D partitioning is essentially an all-to-all personalized communication problem [Ede89]. Hence, all the references in Chapter 4 for all-to-all personalized communication apply directly to matrix transposition. The recursive transposition algorithm, popularly known as RTA, was first reported by Eklundh [Ekl72]. Its adaptations for hypercubes have been described by Bertsekas and Tsitsiklis [BT97], Fox and Furmanski [FF86], Johnsson [Joh87], and McBryan and Van de Velde [MdV87] for one-port communication on each process. Johnsson [Joh87] also discusses parallel RTA for hypercubes that permit simultaneous communication on all channels. Further improvements on the hypercube RTA have been suggested by Ho and Raghunath [HR91], Johnsson and Ho [JH88], Johnsson [Joh90], and Stout and Wagar [SW87].

A number of sources of parallel dense linear algebra algorithms, including those for matrix-vector multiplication and matrix multiplication, are available [CAHH91, GPS90, GL96a, Joh87, Mod88, OS85]. Since dense matrix multiplication is highly computationally intensive, there has been a great deal of interest in developing parallel formulations of this algorithm and in testing its performance on various parallel architectures [Akl89, Ber89, CAHH91, Can69, Cha79, CS88, DNS81, dV89, FJL+88, FOH87, GK91, GL96a, Hip89, HJE91, Joh87, PV80, Tic88]. Some of the early parallel formulations of matrix multiplication were developed by Cannon [Can69], Dekel, Nassimi, and Sahni [DNS81], and Fox et al. [FOH87]. Variants and improvements of these algorithms have been presented by Berntsen [Ber89], and by Ho, Johnsson, and Edelman [HJE91]. In particular, Berntsen [Ber89] presents an algorithm that has strictly smaller communication overhead than Cannon's algorithm, but has a smaller degree of concurrency. Ho, Johnsson, and Edelman [HJE91] present another variant of Cannon's algorithm for a hypercube that permits communication on all channels simultaneously. This algorithm, while reducing communication, also reduces the degree of concurrency. Gupta and Kumar [GK91] present a detailed scalability analysis of several matrix multiplication algorithms. They present an analysis to determine the best algorithm to multiply two n x n matrices on a p-process hypercube for different ranges of n, p and the hardware-related constants. They also show that the improvements suggested by Berntsen and Ho et al. do not improve the overall scalability of matrix multiplication on a hypercube.

Parallel algorithms for LU factorization and solving dense systems of linear equations have been discussed by several researchers [Ber84, BT97, CG87, Cha87, Dav86, DHvdV93, FJL+88, Gei85, GH86, GPS90, GR88, Joh87, LD90, Lei92, Mod88, Mol86 OR88, Ort88, OS86, PR85, Rob90, Saa86, Vav89]. Geist and Heath [GH85, GH86], and Heath [Hea85] specifically concentrate on parallel dense Cholesky factorization. Parallel algorithms for solving triangular systems have also been studied in detail [EHHR88, HR88, LC88, LC89, RO88, Rom87]. Demmel, Heath, and van der Vorst [DHvdV93] present a comprehensive survey of parallel matrix computations considering numerical implications in detail.

A portable software implementation of all matrix and vector operations discussed in this chapter, and many more, is available as PBLAS (parallel basic linear algebra subroutines) [C+95]. The ScaLAPACK library [B+97] uses PBLAS to implement a variety of linear algebra routines of practical importance, including procedures for various methods of matrix factorizations and solving systems of linear equations.

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