In six subjects thermograms of the thighs and the forearms were taken before, during and after 10 min ergometer exercise at 100 W at an ambient temperature of 23 degrees C. During exercise, an intra-individually constant and reproducible skin temperature pattern with local temperature differences exceeding 3 degrees C evolved. Reactions after external local cooling or after occlusion of blood flow and measurements with a laser Doppler-flowmeter showed dispersed convective heat transport to be the source of this irregular pattern. Temperature differences of 3.6 degrees C and deviations of blood flow in the skin microcirculation of 300% within a distance of a few centimetres reduce the value of single-spot measurements of skin temperature with reference to the whole extremity.

No abstract

International audienceWe present a new approach for scheduling independent tasks on multiple CPUs and multiple GPUs. The tasks are assumed to be parallelizable on CPUs using the moldable model: the final number of cores allotted to a task can be decided and set by the scheduler. More precisely, we design an algorithm aiming at minimizing the makespan—the maximum completion time of all tasks—for this scheduling problem. The proposed algorithm combines a dual approximation scheme with a fast integer linear program (ILP). It determines both the partitioning of the tasks, i.e., whether a task should be mapped to CPUs or a GPU, and the number of CPUs allotted to a moldable task if mapped to the CPUs. A worst-case analysis shows that the algorithm has an approximation ratio of 3 2 +. Since the time complexity of the ILP-based algorithm could be non-polynomial, we also present a polynomial-time algorithm with an approximation ratio of 2 +. We complement the theoretical analysis of our two novel algorithms with a simulation study. In these simulations, we compare our algorithms to a modified version of the classical HEFT algorithm, which we adapted to handle moldable tasks. The simulation results show that our algorithm with the 3 2 +-approximation ratio produces significantly shorter schedules than the modified HEFT for most of the instances. In addition, our results provide evidence that our ILP-based algorithm can solve larger problem instances in a reasonable amount of time

Because of the increasing number of cores of current parallel machines and the growing need for a concurrent execution of tasks, the problem of parallel task scheduling is more relevant than ever, especially under the moldable task model, in which tasks are allocated to a fixed number of processors before execution. Much research has been conducted to develop efficient scheduling algorithms for moldable tasks, both in theory and practice. The problem is that theoretical and practical approaches expose shortcomings, for example, many approximation algorithms only guarantee bounds under assumptions, which are unrealistic in practice, or most heuristics have not been rigorously compared with competing approximation algorithms. In particular, it is often assumed that the speedup function of moldable tasks is either non-decreasing, sublinear, or concave. In practice, however, the resulting speedup of parallel programs on current hardware with deep memory hierarchies is most often neither non-decreasing nor concave. We present a new algorithm for the problem of scheduling moldable tasks with precedence constraints for the makespan objective and for arbitrary speedup functions. We show through simulation that the algorithm not only creates competitive schedules for moldable tasks with arbitrary speedup functions but also outperforms other published heuristics and approximation algorithms for non-decreasing speedup functions.We have conducted three different simulation studies, which are part of the overall evaluation. First, we compare the schedules produced by CPA13 and its competitors for the non-increasing and convex run-time model. Second, we address the scalability of these algorithms by increasing the number

No abstract

No abstract

No abstract

No abstract