2390859
Space-efficient scheduling of stochastically generated tasks
We study the problem of scheduling tasks for execution by a processor when the tasks can stochastically generate new tasks. Tasks can be of different types, and each type has a fixed, known probability of generating other tasks. We present results on the random variable S^@s modeling the maximal space needed by the processor to store the currently active tasks when acting under the scheduler @s. We obtain tail bounds for the distribution of S^@s for both offline and online schedulers, and investigate the expected value E[S^@s].
1285833
Quantitative analysis of probabilistic pushdown automata: expectations and variances
Probabilistic pushdown automata (pPDA) have been identified as a natural model for probabilistic programs with recursive procedure calls. Previous works considered the decidability and complexity of the model-checking problem for pPDA and various probabilistic temporal logics. In this paper we concentrate on computing the expected values and variances of various random variables defined over runs of a given probabilistic pushdown automaton. In particular, we show how to compute the expected accumulated reward and the expected gain for certain classes of reward functions. Using these results, we show how to analyze various quantitative properties of pPDA that are not expressible in conventional probabilistic temporal logics.
9857417
Randomized parallel algorithms for backtrack search and branch-and-bound computation
Universal randomized methods for parallelizing sequential backtrack search and branch-and-bound computation are presented. These methods execute on message-passing multi- processor systems, and require no global data structures or complex communication protocols. For backtrack search, it is shown that, uniformly on all instances, the method described in this paper is likely to yield a speed-up within a small constant factor from optimal, when all solutions to the problem instance are required. For branch-and-bound computation, it is shown that, uniformly on all instances, the execution time of this method is unlikely to exceed a certain inherent lower bound by more than a constant factor. These randomized methods demonstrate the effectiveness of randomization in distributed parallel computation. Categories and Subject Descriptors: F.2.2 (Analysis of Algorithms and Problem Complexity): Non-numerical Algorithms-computation
16845848
Space-eecient Scheduling of Nested Parallelism
Many of today's high level parallel languages support dynamic, ne-grained parallelism. These languages allow the user to expose all the parallelism in the program, which is typically of a much higher degree than the number of processors. Hence an eecient scheduling algorithm is required to assign computations to processors at runtime. Besides having low overheads and good load balancing, it is important for the scheduling algorithm to minimize the space usage of the parallel program. This paper presents an on-line scheduling algorithm that is provably space-eecient and time-eecient for nested parallel languages. For a computation with depth D and serial space requirement S 1 , the algorithm generates a schedule that requires at most S 1 + O(p D) space (including scheduler space) on p processors. To allow the scheduler to scale with the number of processors, we also parallelize the scheduler and analyze the space and time bounds of the computation to include scheduling costs. In addition to showing that the scheduling algorithm is space and time eecient in theory, we demonstrate that it is eeective in practice. We have implemented a runtime system that uses our algorithm to schedule lightweight parallel threads. The results of executing parallel programs on this system show that our scheduling algorithm signiicantly reduces memory usage compared to previous techniques, without compromising performance.
40566370
Scheduling multithreaded computations by work stealing
This paper studies the problem of efficiently scheduling fully strict (i.e., well-structured) multithreaded computations on parallel computers. A popular and practical method of scheduling this kind of dynamic MIMD-style computation is "work stealing," in which processors needing work steal computational threads from other processors. In this paper, we give the first provably good work-stealing scheduler for multithreaded computations with dependencies. Specifically, our analysis shows that the expected time T/sub P/ to execute a fully strict computation on P processors using our work-stealing scheduler is T/sub P/=O(T/sub 1//P+T/sub /spl infin//), where T/sub 1/ is the minimum serial execution time of the multithreaded computation and T/sub /spl infin// is the minimum execution time with an infinite number of processors. Moreover, the space S/sub P/ required by the execution satisfies S/sub P//spl les/S/sub 1/P. We also show that the expected total communication of the algorithm is at most O(T/sub /spl infin//S/sub max/P), where S/sub max/ is the size of the largest activation record of any thread, thereby justifying the folk wisdom that work-stealing schedulers are more communication efficient than their work-sharing counterparts. All three of these bounds are existentially optimal to within a constant factor.<<ETX>>
52311200
Adaptive work-stealing with parallelism feedback
Multiprocessor scheduling in a shared multiprogramming environment can be structured as two-level scheduling, where a kernel-level job scheduler allots processors to jobs and a user-level thread scheduler schedules the work of a job on its allotted processors. We present a randomized work-stealing thread scheduler for fork-join multithreaded jobs that provides continual parallelism feedback to the job scheduler in the form of requests for processors. Our A-STEAL algorithm is appropriate for large parallel servers where many jobs share a common multiprocessor resource and in which the number of processors available to a particular job may vary during the job's execution. Assuming that the job scheduler never allots a job more processors than requested by the job's thread scheduler, A-STEAL guarantees that the job completes in near-optimal time while utilizing at least a constant fraction of the allotted processors.
 We model the job scheduler as the thread scheduler's adversary, challenging the thread scheduler to be robust to the operating environment as well as to the job scheduler's administrative policies. For example, the job scheduler might make a large number of processors available exactly when the job has little use for them. To analyze the performance of our adaptive thread scheduler under this stringent adversarial assumption, we introduce a new technique called trim analysis, which allows us to prove that our thread scheduler performs poorly on no more than a small number of time steps, exhibiting near-optimal behavior on the vast majority.
 More precisely, suppose that a job has work T1 and span T∞. On a machine with P processors, A-STEAL completes the job in an expected duration of O(T1/&Ptilde; + T∞ + L lg P) time steps, where L is the length of a scheduling quantum, and &Ptilde; denotes the O(T∞ + L lg P)-trimmed availability. This quantity is the average of the processor availability over all time steps except the O(T∞ + L lg P) time steps that have the highest processor availability. When the job's parallelism dominates the trimmed availability, that is, &Ptilde; ≪ T1/T∞, the job achieves nearly perfect linear speedup. Conversely, when the trimmed mean dominates the parallelism, the asymptotic running time of the job is nearly the length of its span, which is optimal.
 We measured the performance of A-STEAL on a simulated multiprocessor system using synthetic workloads. For jobs with sufficient parallelism, our experiments confirm that A-STEAL provides almost perfect linear speedup across a variety of processor availability profiles. We compared A-STEAL with the ABP algorithm, an adaptive work-stealing thread scheduler developed by Arora et al. [1998] which does not employ parallelism feedback. On moderately to heavily loaded machines with large numbers of processors, A-STEAL typically completed jobs more than twice as quickly as ABP, despite being allotted the same number or fewer processors on every step, while wasting only 10% of the processor cycles wasted by ABP.
15537880
On the convergence of Newton's method for monotone systems of polynomial equations
Monotone systems of polynomial equations (MSPEs) are systems of fixed-point equations X1 = f1(X1, ..., Xn), ..., Xn = fn(X1, ..., Xn) where each fi is a polynomial with positive real coefficients. The question of computing the least non-negative solution of a given MSPE X = f(X) arises naturally in the analysis of stochastic context-free grammars, recursive Markov chains, and probabilistic pushdown automata. While the Kleene sequence f(0), f(f(0)), ... always converges to the least solution mu.f, if it exists, the number of iterations needed to compute the first i bits of mu.f may grow exponentially in i.Etessami and Yannakakis have recently adapted Newton's iterative method to MSPEs and proved that the Newton sequence converges at least as fast as the Kleene sequence and exponentially faster in many cases.They conjecture that, given an MSPE of size m, the number of Newton iterations needed to obtain i accurate bits of mu.f grows polynomially in i and m. In this paper we show that the number of iterations grows linearly in i for strongly connected MSPEs and may grow exponentially in m for general MSPEs.
7698434
Convergence Thresholds of Newton's Method for Monotone Polynomial Equations
Monotone systems of polynomial equations (MSPEs) are systems of 
 fixed-point equations $X_1 = f_1(X_1, ldots, X_n),$ $ldots, X_n = 
 f_n(X_1, ldots, X_n)$ where each $f_i$ is a polynomial with 
 positive real coefficients. The question of computing the least 
 non-negative solution of a given MSPE $vec X = vec f(vec X)$ 
 arises naturally in the analysis of stochastic models such as 
 stochastic context-free grammars, probabilistic pushdown automata, 
 and back-button processes. Etessami and Yannakakis have recently 
 adapted Newton's iterative method to MSPEs. In a previous paper we 
 have proved the existence of a threshold $k_{vec f}$ for strongly 
 connected MSPEs, such that after $k_{vec f}$ iterations of 
 Newton's method each new iteration computes at least 1 new bit of 
 the solution. However, the proof was purely existential. In this 
 paper we give an upper bound for $k_{vec f}$ as a function of the 
 minimal component of the least fixed-point $muvec f$ of $vec 
 f(vec X)$. Using this result we show that $k_{vec f}$ is at most 
 single exponential resp. linear for strongly connected MSPEs 
 derived from probabilistic pushdown automata resp. from 
 back-button processes. Further, we prove the existence of a 
 threshold for arbitrary MSPEs after which each new iteration 
 computes at least $1/w2^h$ new bits of the solution, where $w$ and 
 $h$ are the width and height of the DAG of strongly connected 
 components.
14287817
Recursive Markov chains, stochastic grammars, and monotone systems of nonlinear equations
We define Recursive Markov Chains (RMCs), a class of finitely presented denumerable Markov chains, and we study algorithms for their analysis. Informally, an RMC consists of a collection of finite-state Markov chains with the ability to invoke each other in a potentially recursive manner. RMCs offer a natural abstract model for probabilistic programs with procedures. They generalize, in a precise sense, a number of well-studied stochastic models, including Stochastic Context-Free Grammars (SCFG) and Multi-Type Branching Processes (MT-BP).
 We focus on algorithms for reachability and termination analysis for RMCs: what is the probability that an RMC started from a given state reaches another target state, or that it terminates? These probabilities are in general irrational, and they arise as (least) fixed point solutions to certain (monotone) systems of nonlinear equations associated with RMCs. We address both the qualitative problem of determining whether the probabilities are 0, 1 or in-between, and the quantitative problems of comparing the probabilities with a given bound, or approximating them to desired precision.
 We show that all these problems can be solved in PSPACE using a decision procedure for the Existential Theory of Reals. We provide a more practical algorithm, based on a decomposed version of multi-variate Newton's method, and prove that it always converges monotonically to the desired probabilities. We show this method applies more generally to any monotone polynomial system. We obtain polynomial-time algorithms for various special subclasses of RMCs. Among these: for SCFGs and MT-BPs (equivalently, for 1-exit RMCs) the qualitative problem can be solved in P-time; for linearly recursive RMCs the probabilities are rational and can be computed exactly in P-time.
 We show that our PSPACE upper bounds cannot be substantially improved without a breakthrough on long standing open problems: the square-root sum problem and an arithmetic circuit decision problem that captures P-time on the unit-cost rational arithmetic RAM model. We show that these problems reduce to the qualitative problem and to the approximation problem (to within any nontrivial error) for termination probabilities of general RMCs, and to the quantitative decision problem for termination (extinction) of SCFGs (MT-BPs).
124066830
Model checking probabilistic pushdown automata
We consider the model checking problem for probabilistic pushdown automata (pPDA) and properties expressible in various probabilistic logics. We start with properties that can be formulated as instances of a generalized random walk problem. We prove that both qualitative and quantitative model checking for this class of properties and pPDA is decidable. Then, we show that model checking for the qualitative fragment of the logic PCTL and pPDA is also decidable. Moreover, we develop an error-tolerant model checking algorithm for general PCTL and the subclass of stateless pPDA. Finally, we consider the class of properties definable by deterministic Buchi automata, and show that both qualitative and quantitative model checking for pPDA is decidable.
14229059
On the Memory Consumption of Probabilistic Pushdown Automata
We investigate the problem of evaluating memory consumption for systems modelled by probabilistic pushdown automata (pPDA). The space needed by a runof a pPDA is the maximal height reached by the stack during the run. Theproblem is motivated by the investigation of depth-first computations that playan important role for space-efficient schedulings of multithreaded programs. 
We study the computation of both the distribution of the memory consumption and its expectation. For the distribution, we show that a naive method incurs anexponential blow-up, and that it can be avoided using linear equation systems.We also suggest a possibly even faster approximation method.Given~$\varepsilon>0$, these methods allow to compute bounds on the memoryconsumption that are exceeded with a probability of at most~$\varepsilon$. 
For the expected memory consumption, we show that whether it is infinite can be decided in polynomial time for stateless pPDA (pBPA) and in polynomial space for pPDA. We also provide an iterative method for approximating theexpectation. We show how to compute error bounds of our approximation methodand analyze its convergence speed. We prove that our method convergeslinearly, i.e., the number of accurate bits of the approximation is a linear function of the number of iterations.
124656002
On distribution tails and expectations of maxima in critical branching processes
We derive the limit behaviour of the distribution tail of the global maximum of a critical Galton-Watson process and also of the expectations of partial maxima of the process, when the offspring law belongs to the domain of attraction of a stable law. Thus the Lindvall (1976) and Athreya (1988) results are extended to the infinite variance case. It is shown that in the general case these two asymptotics are closely related to each other, and the latter follows readily from the former. We also discuss a related problem from the theory of general branching processes.
9486619
An Extension of Newton's Method to omega -Continuous Semirings
Fixed point equations x = F (x) over ω-continuous semirings are a natural mathematical foundation of interprocedural program analysis. Equations over the semiring of the real numbers can be solved numerically using Newton's method. We generalize the method to any ω-continuous semiring and show that it converges faster to the least fixed point than the Kleene sequence 0, F(0),F (F (0)), . . . We prove that the Newton approximants in the semiring of languages coincide with finite-index approximations studied by several authors in the 1960s. Finally, we apply our results to the analysis of stochastic context-free grammars.
12510680
Complexity and Real Computation
1 Introduction.- 2 Definitions and First Properties of Computation.- 3 Computation over a Ring.- 4 Decision Problems and Complexity over a Ring.- 5 The Class NP and NP-Complete Problems.- 6 Integer Machines.- 7 Algebraic Settings for the Problem "P ? NP?".- 8 Newton's Method.- 9 Fundamental Theorem of Algebra: Complexity Aspects.- 10 Bezout's Theorem.- 11 Condition Numbers and the Loss of Precision of Linear Equations.- 12 The Condition Number for Nonlinear Problems.- 13 The Condition Number in ?(H(d).- 14 Complexity and the Condition Number.- 15 Linear Programming.- 16 Deterministic Lower Bounds.- 17 Probabilistic Machines.- 18 Parallel Computations.- 19 Some Separations of Complexity Classes.- 20 Weak Machines.- 21 Additive Machines.- 22 Nonuniform Complexity Classes.- 23 Descriptive Complexity.- References.
43188859
Matrix analysis
Linear algebra and matrix theory are fundamental tools in mathematical and physical science, as well as fertile fields for research. This new edition of the acclaimed text presents results of both classic and recent matrix analyses using canonical forms as a unifying theme, and demonstrates their importance in a variety of applications. The authors have thoroughly revised, updated, and expanded on the first edition. The book opens with an extended summary of useful concepts and facts and includes numerous new topics and features, such as: - New sections on the singular value and CS decompositions - New applications of the Jordan canonical form - A new section on the Weyr canonical form - Expanded treatments of inverse problems and of block matrices - A central role for the Von Neumann trace theorem - A new appendix with a modern list of canonical forms for a pair of Hermitian matrices and for a symmetric-skew symmetric pair - Expanded index with more than 3,500 entries for easy reference - More than 1,100 problems and exercises, many with hints, to reinforce understanding and develop auxiliary themes such as finite-dimensional quantum systems, the compound and adjugate matrices, and the Loewner ellipsoid - A new appendix provides a collection of problem-solving hints.
118697980
Nonnegative Matrices in the Mathematical Sciences
1. Matrices which leave a cone invariant 2. Nonnegative matrices 3. Semigroups of nonnegative matrices 4. Symmetric nonnegative matrices 5. Generalized inverse- Positivity 6. M-matrices 7. Iterative methods for linear systems 8. Finite Markov Chains 9. Input-output analysis in economics 10. The Linear complementarity problem 11. Supplement 1979-1993 References Index.