We introduce a transformation system for concurrent constraint programming (CCP). We define suitable applicability conditions for the transformations which guarantee that the input output CCP semantics is preserved also when distinguishing deadlocked computations from successful ones and when considering intermediate results of (possibly) non-terminating computations. The system allows us to optimize CCP programs while preserving their intended meaning: In addition to the usual benefits that one has for sequential declarative languages, the transformation of concurrent programs can also lead to the elimination of communication channels and of synchronization points, to the transformation of non-deterministic computations into deterministic ones, and to the crucial saving of computational space. Furthermore, since the transformation system preserves the deadlock behavior of programs, it can be used for proving deadlock freeness of a given program wrt a class of queries. To this aim it is sometimes sufficient to apply our transformations and to specialize the resulting program wrt the given queries in such a way that the obtained program is trivially deadlock free.
As previously mentioned, differently from our case in @cite_9 it is considered a definition of which allows us to remove potentially selectable branches; the consequence is that the resulting transformation system is only (thus not totally) correct. We should mention that in @cite_9 two preliminary assumptions on the scheduling'' are made in such a way that this limitation is actually less constraining than it might appear.
@cite_9
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Modern dynamically scheduled processors use branch prediction hardware to speculatively fetch and execute most likely executed paths in a program. Complex branch predictors have been proposed which attempt to identify these paths accurately such that the hardware can benefit from out-of-order (OOO) execution. Recent studies have shown that inspite of such complex prediction schemes, there still exist many frequently executed branches which are difficult to predict. Predicated execution has been proposed as an alternative technique to eliminate some of these branches in various forms ranging from a restrictive support to a full-blown support. We call the restrictive form of predicated execution as guarded execution. In this paper, we propose a new algorithm which uses profiling and selectively performs if-conversion for architectures with guarded execution support. Branch profiling is used to gather the taken, non-taken and misprediction counts for every branch. This combined with block profiling is used to select paths which suffer from heavy mispredictions and are profitable to if-convert. Effects of three different selection criterias, namely size-based, predictability-based and profiled-based, on net cycle improvements, branch mispredictions and mis-speculated instructions are then studied. We also propose new mechanisms to convert unsafe instructions to safe form to enhance the applicability of the technique. Finally, we explain numerous adjustments that were made to the selection criterias to better reflect the OOO processor behavior. Abstract In this paper, we present a unifying analysis for redundancy systems with cancel-on-start ( c . o . s . ) and cancel-on-complete ( c . o . c . ) with exponentially distributed service requirements. With c . o . s . ( c . o . c . ) all redundant copies are removed as soon as one of the copies starts (completes) service. As a consequence, c . o . s . does not waste any computing resources, as opposed to c . o . c . We show that the c . o . s . model is equivalent to a queueing system with multi-type jobs and servers, which was analyzed in , (2012), and show that c . o . c . (under the assumption of i.i.d. copies) can be analyzed by a generalization of , (2012) where state-dependent departure rates are permitted. This allows us to show that the stationary distribution for both the c . o . c . and c . o . s . models has a product form. We give a detailed first-time analysis for c . o . s and derive a closed form expression for important metrics like mean number of jobs in the system, and probability of waiting. We also note that the c . o . s . model is equivalent to Join-Shortest-Work queue with power of d (JSW( d )). In the latter, an incoming job is dispatched to the server with smallest workload among d randomly chosen ones. Thus, all our results apply mutatis-mutandis to JSW( d ). Comparing the performance of c . o . s . with that of c . o . c . with i.i.d. copies gives the unexpected conclusion (since c . o . s . does not waste any resources) that c . o . s . is worse in terms of mean number of jobs. As part of ancillary results, we illustrate that this is primarily due to the assumption of i.i.d. copies in case of c . o . c . (together with exponentially distributed requirements) and that such assumptions might lead to conclusions that are qualitatively different from that observed in practice. We study the problem of non-preemptive scheduling to minimize energy consumption for devices that allow dynamic voltage scaling. Specifically, consider a device that can process jobs in a non-preemptive manner. The input consists of (i) the set R of available speeds of the device, (ii) a set J of jobs, and (iii) a precedence constraint Π among J. Each job j in J, defined by its arrival time aj, deadline dj, and amount of computation cj, is supposed to be processed by the device at a speed in R. Under the assumption that a higher speed means higher energy consumption, the power-saving scheduling problem is to compute a feasible schedule with speed assignment for the jobs in J such that the required energy consumption is minimized. This paper focuses on the setting of weakly dynamic voltage scaling, i.e., speed change is not allowed in the middle of processing a job. To demonstrate that this restriction on many portable power-aware devices introduces hardness to the power-saving scheduling problem, we prove that the problem is NP-hard even if aj = aj ′ and dj = dj ′ hold for all j,j ′∈ Jand |R|=2. If |R|<∞, we also give fully polynomial-time approximation schemes for two cases of the general NP-hard problem: (a) all jobs share a common arrival time, and (b) Π = ∅ and for any j,j ′ ∈ J, aj ≤ aj ′ implies dj ≤ dj ′. To the best of our knowledge, there is no previously known approximation algorithm for any special case of the NP-hard problem. We investigate the exact solution of the vehicle routing problem with time windows, where multiple trips are allowed for the vehicles. In contrast to previous works in the literature, we specifically consider the case in which it is mandatory to visit all customers and there is no limitation on duration. We develop two branch-and-price frameworks based on two set covering formulations: a traditional one where columns (variables) represent routes, that is, a sequence of consecutive trips, and a second one in which columns are single trips. One important difficulty related to the latter is the way mutual temporal exclusion of trips can be handled. It raises the issue of time discretization when solving the pricing problem. Our dynamic programming algorithm is based on concept of groups of labels and representative labels. We provide computational results on modified small-sized instances (25 customers) from Solomon’s benchmarks in order to evaluate and compare the two methods. Results show that some difficult instances are out of reach for the first branch-and-price implementation, while they are consistently solved with the second.