Spark is a new promising platform for scalable data-parallel computation. It provides several high-level application programming interfaces (APIs) to perform parallel data aggregation. Since execution of parallel aggregation in Spark is inherently non-deterministic, a natural requirement for Spark programs is to give the same result for any execution on the same data set. We present PURESPARK, ...

This thesis presents a new, Java-based object-oriented parallel language called Deterministic Parallel Java (DPJ). DPJ uses a novel effect system to guarantee determinism by default. That means that parallel programs are guaranteed to execute deterministically unless nondeterminism is explicitly requested. This is in contrast to the shared-memory models in widespread use today, such as threads ...

We give an efficient deterministic parallel approximation algorithm for the minimumweight vertexand set-cover problems and their duals (edge/element packing). The algorithm is simple and suitable for distributed implementation. It fits no existing paradigm for fast, efficient parallel algorithms — it uses only “local” information at each step, yet is deterministic. (Generally, such algorithms h...

This paper presents a method for the evolution of deterministic finite automata that combines genetic programming and cellular encoding. Programs are evolved that specify actions for the incremental growth of a deterministic finite automata from an initial single-state zygote. The results show that, given a test bed of positive and negative samples, the proposed method is successful at inducing...

We consider the stochastic identical parallel machine scheduling problem and its online extension, when the objective is to minimize the expected total weighted completion time of a set of jobs that are released over time. We give randomized as well as deterministic online and offline algorithms that have the best known performance guarantees in either setting, online or offline and determinist...

We investigate the problem of sequencing jobs that have multiple components. Each component of the job needs to be processed independently on a specified machine. We derive approximate algorithms for the problem of scheduling such vector jobs to minimize their total completion time in the deterministic as well as stochastic setting. In particular, we propose a Linear Programming and a Greedy he...

We consider the problem of minimizing the total weighted completion time on identical parallel machines when jobs have stochastic processing times and may arrive over time. We give randomized as well as deterministic online and off-line algorithms that have the best known performance guarantees in either setting, deterministic and offline or randomized and online. Our analysis is based on a nov...

uncertainty is undoubtedly an inevitable part of programming process. a lot of effective factors in the water resources’ performance are not deterministic while their values are surely needed for the design and implantation of water systems. since an optimal design of water distribution systems is in demand of a just evaluation of the costs, efficient operation of such systems calls for a prope...