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Parallel Computing


I got interested in parallel computing in 1980 in Australia by working with Les Goldschlager. I carried that interest into my PhD thesis and on into the 1990s.

Parallel Prefix (2006)

Parallel prefix circuit thumbnails.

M. Sheeran and I. Parberry, "A new approach to the design of optimal parallel prefix circuits". Technical Report No. 2006:1, Department of Computer Science and Engineering, Chalmers University of Technology, Göteborg, Sweden, 2006. [pdf]


Parallel prefix is one of the fundamental algorithms in computer science. Parallel prefix networks are used to compute carries in fast addition circuits, and have a number of other applications, including the computation of linear recurrences and loop parallelization. A new construction, called Slices, for fan-out-constrained depth size optimal (DSO) parallel prefix circuits is presented. The construction encompasses the largest possible number of inputs for given depth and fan-out. The construction improves on previous approaches that produce DSO networks with constrained fan-out by encompassing more inputs for a given depth. Even when compared with parallel prefix circuits with unbounded fan-out, the construction provides a new family of circuits that are both small and reasonably shallow. We present the construction, which is composed of recursively designed blocks, and derive a recurrence for the maximum number of inputs that can be processed for a given fan-out and depth. We also show how a DSO network built according to our construction can be cropped, to produce a new DSO network with the same depth and fan-out, but fewer inputs. Thus, we can produce a DSO network for given depth, fan-out and number of inputs, provided such a network exists. We believe that we are the first to be able to do this. The resulting networks are compared to others with both bounded and unbounded fan-out.

Author's Comments

Mary visited me in Texas in 2005, during which time she managed to reinfect me with an interest in parallel prefix circuits. This never got beyond the Tech Report version, but it surprised us by garnering a significant number of citations.

Load Sharing (1995)

Parallel priority queue thumbnails.

I. Parberry, "Load Sharing with Parallel Priority Queues". Journal of Computer and System Sciences, Vol. 50, No. 1, pp. 64-73, 1995. [pdf]


For maximum efficiency in a multiprocessor system the load should be shared evenly over all processors, that is, there should be no idle processors when tasks are available. The delay in a load sharing algorithm is the larger of the maximum time that any processor can be idle before a task is assigned to it, and the maximum time that it must wait to be relieved of an excess task. A simple parallel priority queue architecture for load sharing in a $p$-processor multiprocessor system is proposed. This architecture uses $O(p \log(n/p))$ special-purpose processors (where $n$ is the maximal size of the priority queue), an interconnection pattern of bounded degree, and achieves delay $O(\log p)$, which is optimal for any bounded degree system.

Author's Comments

I still think ragged heaps are incredibly cool.

PRAM Lower Bounds (1991)

P. Y. Yan and I. Parberry, "Improved Upper and Lower Time Bounds for Parallel Random Access Machines Without Simultaneous Writes", SIAM Journal on Computing, Vol. 20, No. 1, pp. 88-99, 1991. [pdf]


The time required by a variant of the PRAM (a parallel machine model which consists of sequential processors which communicate by reading and writing into a common shared memory) to compute a certain class of functions called critical functions (which include the Boolean OR of $n$ bits) is studied. Simultaneous reads from individual cells of the shared-memory are permitted, but simultaneous writes are not. It is shown that any PRAM which computes a critical function must take at least $0.5 \log n - O(1)$ steps, and that there exists a critical function which can be computed in $0.57 \log n + O(1)$ steps. These bounds represent an improvement in the constant factor over those previously known.

Author's Comments

My PhD student P. Y. Yan proved the lower bounds and I proved the upper bounds. He was doing a math PhD at the time, and I was his adviser.

Oblivious Routing (1990)

I. Parberry, "An Optimal Time Bound for Oblivious Routing". Algorithmica, Vol. 5, No. 2, pp. 243-251, 1990.


We consider the problem of routing data packets in a constant-degree network of synchronous processors. A routing scheme is called oblivious if the route taken by each packet is uniquely determined by its source and destination. The time required for the oblivious routing of n packets on $n$ processors is known to be $\Theta(\sqrt{n})$. We extend this result to show that the time required for the oblivious routing of n packets on P(n) processors is $\Theta(n/\sqrt{P(n)} + \log n)$.

Author's Comments

The adjective oblivious is one of Mike Paterson's terms. I still like it.

Nondeterministic Parallel Computation (1989)

I. Parberry, "A Note on Nondeterminism in Small, Fast Parallel Computers". IEEE Transactions on Computers, Vol. 38, No. 5, pp. 766-767, 1989. [pdf]


Nondeterministic analogues of the well-known language classes NC and SC called NNC and NSC, respectively, are investigated. NC is the class of languages that can be accepted by small, fast parallel computers; SC is the class of languages that can be recognized by a deterministic Turing machine in polynomial time and polylog tape-head reversals. Adding nondeterminism to SC leaves it in the domain of parallel computation since NSC contained in POLYLOGSPACE. That is, NSC is a subset of the class of languages computable by fast parallel computers. Adding nondeterminism to NC appears to make it much more powerful since NNC=NP. It is clear that NSC contained in NNC, and probable that NSC contained in/implied by NNC. Further evidence for this conjecture is provided by showing that NSC is precisely the class of languages recognizable in simultaneous polynomial time and polylog reversals by a nondeterministic Turing machine with a read-only input tape and a single read-write work tape; it is known that NNC is similar, but is recognizable by a Turing machine with two read-write tapes.

Parallel Sorting (1989, 1990)

I. Parberry, Scholarly Review of Parallel Sorting. ACM Computing Reviews, Vol. 30, No. 11, pp. 578-580, 1989 (Review Number 8909-0816). Reprinted in Michael Loui's "Reprints from Computing Reviews", SIGACT News, Vol. 21, No. 1, pp. 14-17, 1990. [pdf]

From the Preamble

Early research into sorting focused on in situ comparison-based sorting algorithms . Such an algorithm is said to be oblivious (a term which can be traced, in a different context, to Paterson) if the sequence of cells accessed is dependent on the number of cells but not on their contents . An oblivious algorithm has a particularly elegant hardware implementation called a sorting network, which consists of a circuit constructed without fan-out from building blocks called comparators. Comparators have two inputs and two outputs, and swap the values on the inputs if they are out of order, passing them through unchanged otherwise . The depth (number of layers) of the circuit is equal to the number of phases of nonoverlapping comparisons, and the size (number of comparators) of the circuit is equal to the number of comparisons used in the oblivious sorting algorithm . Note that size is bounded above by $n/2$ times the depth.

Simulation by a Turing Machine (1987)

I. Parberry, "An Improved Simulation of Space and Reversal Bounded Deterministic Turing Machines by Width and Depth Bounded Uniform Circuits". Information Processing Letters, Vol. 24, No. 6, pp. 363-367, 1987. [pdf]


We present an improved simulation of space and reversal bounded Turing machines by width and depth bounded uniform circuits. (All resource bounds hold simultaneously). An $S(n)$ space, $R(n)$ reversal bounded deterministic $k$-tape Turing machine can be simulated by a uniform circuit of depth $O(R(n) \log^2 S(n))$ and width $O(S(n)^k)$. Our proof is cleaner, and has slightly better resource bounds than the original proof due to Pippenger. The improvement in resource bounds comes primarily from the use of a shared-memory machine instead of an oblivious Turing machine, and the concept of a special situation.

Author's Comments

My PhD thesis adviser Mike Paterson had a hand in this, polishing my proofs and getting some extra improvements in the bounds. He refused coauthorship, although I believed at the time and still believe that he was entitled to it.

Parallel Complexity Theory (1987)

Cover image.

I. Parberry, Parallel Complexity Theory, in series Research Notes in Theoretical Computer Science, (R. V. Book, Ed.), Pitman Press, London, 1987.

From the Preface

Parallel complexity theory, the study of resource-bounded parallel computation, is surely one of the fastest-growing areas of theoretical Computer Science. In the light of this, it would be foolish to attempt an encyclopedic coverage of the field. However, it is the belief of the author that its foundations are becoming increasingly clear and well-defined. This Monograph is an attempt to present these foundations in a unified and coherent manner.

The material contained herein is aimed at advanced Graduate students or researchers in theoretical Computer Science who wish to gain an insight into parallel complexity theory. It is assumed that the reader has (in addition to a certain level of mathematical maturity) a general knowledge of Computer Science, and familiarity with automata theory, formal languages, complexity theory and analysis of algorithms.

Author's Comments

This is the book version of my thesis with a few additions, including an integer version of Mike Paterson's treatment of the AKS sorting network.

Simulation of Parallel Computers (1985, 1987)

I. Parberry, "Some Practical Simulations of Impractical Parallel Computers". Parallel Computing, Vol. 4, No. 1, pp. 93-101, 1987. A preliminary version of this paper appeared in Proceedings of the International Workshop on Parallel Computing and VLSI, Amalfi, Italy, May 1984, pp. 27-37, (North Holland, 1985).


Many popular theoretical models of parallel computers suffer the drawback of being highly impractical. The aim of this paper is to examine simulations of two impractical parallel machine models (global memory machines and networks of sequential processors) by two more practical models (uniform circuits and feasible networks). We give a single basic simulation theorem which epitomizes a number of related results in this area. In particular, one corollary to this theorem is an improved simulation of space and reversal bounded Turing machines by width and depth bounded uniform circuits. We are thus able to unify Pippenger's characterization of NC with current work on universal parallel machines.

Interconnection Patterns (1986)

Interconnection graphs.

I. Parberry, "On Recurrent and Recursive Interconnection Patterns". Information Processing Letters, Vol. 22, No. 6, pp. 285-289, 1986. [pdf]


A number of graphs, in particular variants of the cube-connected cycles and shuffle-exchange, have become popular as interconnection patterns for synchronous parallel computers. A useful property is to have a large machine built from isomorphic copies of a smaller one, plus a few extra processors. If only a small number of extra processors have to be added, we call the interconnection pattern recurrent. If no extra processors are added, we call it recursive. We show that a recursive interconnection pattern is, in a sense, not as versatile as the cube-connected cycles or shuffle-exchange. However, we present a recurrent interconnection pattern which is.

The Parallel Computation Thesis (1986)

I. Parberry, "Parallel Speedup of Sequential Machines: A Defense of the Parallel Computation Thesis", SIGACT News, Vol. 18, No. 1, pp. 54-67, 1986. [pdf]


It is reasonable to expect parallel machines to be faster than sequential ones. But exactly how much faster do we expect them to be? Various authors have observed that an exponential speedup is possible if sufficiently many processors are available. One such author has claimed (erroneously) that this is a counterexample to the parallel computation thesis. We show that even more startling speedups are possible, in fact if enough processors are used, any recursive function can be computed in constant time. Far from contradicting the parallel computation thesis, this result actually provides further evidence in favour of it. Also, we show that an arbitrary polynomial speedup of sequential machines is possible on a model which satisfies the parallel computation thesis. If, as widely conjectured, P is not contained in POLYLOGSPACE, then there can be no exponential speedup on such a model.

Author's Comments

I wrote this paper because I was annoyed by the temerity of another author saying that the parallel computation thesis was dead because exponential speedups are possible with exponentially many processors. That's quite consistent with the parallel computation thesis.

Parallel Prime Number Sieves (1981, 1994)

J. Sorenson and I. Parberry, "Two Fast Parallel Prime Number Sieves", Information and Computation, Vol. 114, No. 1, pp. 115-130, 1994. [pdf]

Part of this paper was published in I. Parberry, "Parallel Speedup of Sequential Prime Number Sieves", Technical Report No. 30, Department of Computer Science, University of Queensland, 1981.


A prime number sieve is an algorithm that lists all prime numbers up to a given bound $n$. Two parallel prime number sieves for an algebraic EREW PRAM model of computation are presented and analyzed. The first sieve runs in $O(\log n)$ time using $O(n/(\log n \log \log n))$ processors, and the second sieve runs in $O(\sqrt{n})$ time using $O(\sqrt{n})$ processors. The first sieve is optimal in the sense that it performs work $O(n/\log \log n)$, which is within a constant factor of the number of arithmetic operations used by the fastest known sequential prime number sieves. However, when both sieves are analyzed on the Block PRAM model as defined by Aggarwal, Chandra, and Snir, it is found that the second sieve is more work-efficient when communication latency is significant

Author's Comments

I was a TA for Paul Pritchard in 1981 when he came into class very excitedly and describer his wheel sieve, the first significant improvement to the Sieve of Eratosthenes. Add that idea to Les Goldschlager's preoccupation with parallel computers, and you get me parallelizing Paul's wheel sieve. It became a technical report at the University of Queencsland, but I never did quite trust my ability to do number theory proofs enough to submit it to a journal. In the 1990s Jon Sorenson emailed me with a new parallel sieve technique of his own, and verified that my proofs were indeed correct. We pooled our ideas and came up with a joint paper.

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