/pdf/convex-analysisnoer-san-nojin-zhan-nituite-5dl2rbmoyr.pdf

Convex Analysisの二,三の進展について

Introduction to linear and nonlinear programming

Convex Analysis: (pms-28)

An Introduction to Probability Theory and its Applications, Volume I

In many places, inserted text is highlighted in red. Preface p. xx " all of which are useful in structuring concurrent applications. " Chapter 1 p. 4 In Fig. 1.1, line 4 should say for (int j = (i * block) + 1; j <= (i + 1)* block; j++) { p. 11 p. 18 Second paragraph of Mutual Exclusion bullet: " Initially the can is either up or down. Let us say it was down. Then only the pets can go in, and mutual exclusion holds. " Replace question 6 bullet 2 with:  Suppose the method M accounts for 30% of the program's computation time. Let sn be the program's speedup on n processes, assuming the rest of the program is perfectly parallelizable. Your boss tells you to double this speedup: the revised program should have speedup s'n ≥ sn/2. You advertize for a programmer to replace M with an improved version, k times faster. What value of k should you require? Chapter 2 p. 23 Figure 2.3 should be amended as shown: public long getAndIncrement() { lock.lock(); try { long temp = value; value = temp + 1; return temp; } finally { lock.unlock(); } } p. 25 Pragma 2.3.1: " We explain the reasons in Chapter 3 and Appendix B. " p. 26 In Fig. 2.5, remove all declarations of variables as volatile. Indeed as stated in Pragma 2.3.1 on page 25, one should use memory barriers when implementing these algorithms. However, in this chapter (in all code) we avoid such issues to keep the algorithms simple. Adding volatile to these variables would require more complex coding that would distract readers from the core issues at hand.

The Art of Multiprocessor Programming

Many scientists perform extensive computations by executing large bags of similar tasks (BoTs) in mixtures of computational environments, such as grids and clouds. Although the reliability and cost may vary considerably across these environments, no tool exists to assist scientists in the selection of environments that can both fulfill deadlines and fit budgets. To address this situation, we introduce the Expert BoT scheduling framework. Our framework systematically selects from a large search space the Pareto-efficient scheduling strategies, that is, the strategies that deliver the best results for both make span and cost. Expert chooses from them the best strategy according to a general, user-specified utility function. Through simulations and experiments in real production environments, we demonstrate that Expert can substantially reduce both make span and cost in comparison to common scheduling strategies. For bioinformatics BoTs executed in a real mixed grid + cloud environment, we show how the scheduling strategy selected by Expert reduces both make span and cost by 30%-70%, in comparison to commonly-used scheduling strategies.

/pdf/expert-pareto-efficient-task-replication-on-grids-and-a-3yec2bpsmi.pdf

ExPERT: Pareto-Efficient Task Replication on Grids and a Cloud

We present a holistic approach for efficient execution of bags-of-tasks (BOTs) on multiple grids, clusters, and volunteer computing grids virtualized as a single computing platform. The challenge is twofold: to assemble this compound environment and to employ it for execution of a mixture of throughput- and performance-oriented BOTs, with a dozen to millions of tasks each. Our generic mechanism allows per BOT specification of dynamic arbitrary scheduling and replication policies as a function of the system state, BOT execution state, and BOT priority.We implement our mechanism in the GridBot system and demonstrate its capabilities in a production setup. GridBot has executed hundreds of BOTs with over 9 million jobs during three months alone; these have been invoked on 25,000 hosts, 15,000 from the Superlink@Technion community grid and the rest from the Technion campus grid, local clusters, the Open Science Grid, EGEE, and the UW Madison pool.

GridBot: execution of bags of tasks in multiple grids

The configuration of a distributed storage system typically includes, among other parameters, the set of servers and their roles in the replication protocol. Although mechanisms for changing the configuration at runtime exist, it is usually left to system administrators to manually determine the "best" configuration and periodically reconfigure the system, often by trial and error. This paper describes a new workload-driven optimization framework that dynamically determines the optimal configuration at run-time. We focus on optimizing leader and quorum based replication schemes and divide the framework into three optimization tiers, dynamically optimizing different configuration aspects: 1) leader placement, 2) roles of different servers in the replication protocol, and 3) replica locations. We showcase our optimization framework by applying it to a large-scale distributed storage system used internally in Google and demonstrate that most client applications significantly benefit from using our framework, reducing average operation latency by up to 94%.

/pdf/take-me-to-your-leader-online-optimization-of-distributed-587ecq07eu.pdf

Take me to your leader!: online optimization of distributed storage configurations

Nearly tight lower and upper bounds on the capacity of generalized Ising channels are presented. For the case where feedback is allowed, a closed-form expression for the capacity is found for channel error probability p ∈ [0, p 0 ], where p 0 ≈ 0.398324. Two lower bounds on that capacity for larger values of p are presented.

On the capacity of generalized Ising channels

A new variable-rate coding technique is presented for two-dimensional constraints. For certain constraints, such as the (0, 2)-RLL, (2, infin)-RLL, and the "no isolated bits" (n.i.b.) constraints, the technique is shown to improve on previously- published lower bounds on the capacity of the constraint.

Two-dimensional constrained coding based on tiling

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