BinLPT: A Novel Workload-Aware Loop Scheduler for Irregular Parallel Loops

Workload-aware loop schedulers were introduced to deliver better performance than classical strategies, but they present limitations on workload estimation, chunk scheduling and integrability with applications. Targeting these challenges, in this work we propose a novel workload-aware loop scheduler that is called BinLPT and it is based on three features. First, it relies on some user-supplied estimation of the workload of the target parallel loop. Second, BinLPT uses a greedy bin packing heuristic to adaptively partition the iteration space in several chunks. The maximum number of chunks to be produced is a parameter that may be fine-tuned. Third, it schedules chunks of iterations using a hybrid scheme based on the LPT rule and on-demand scheduling. We integrated BinLPT in OpenMP, and we evaluated its performance in a large-scale NUMA machine using a synthetic kernel and 3D N-Body Simulations. Our results revealed that BinLPT improves performance over OpenMP’s strategies by up to 45.13% and 37.15% in the synthetic and application kernels, respectively.

Optimal Rules for Single Machine Scheduling with Stochastic Breakdowns

This paper studies the problem of scheduling a set of jobs on a single machine subject to stochastic breakdowns, where jobs have to be restarted if preemptions occur because of breakdowns. The breakdown process of the machine is independent of the jobs processed on the machine. The processing times required to complete the jobs are constants if no breakdown occurs. The machine uptimes are independently and identically distributed (i.i.d.) and are subject to a uniform distribution. It is proved that theLongest Processing Time first(LPT) rule minimizes the expected makespan. For the large-scale problem, it is also showed that theShortest Processing Time first(SPT) rule is optimal to minimize the expected total completion times of all jobs.