Scheduling plays an important role in improving the performance of big data-parallel processing. Spark is an in-memory parallel computing framework that uses a multi-threaded model in task scheduling. Most Spark
parallel processingphysics computingscheduling/ memory-based schedulingscientific computing clustersmemory utilisationthroughputThis study looks at how increased memory utilisation affects throughput and energy consumption in scientific computing, especially in high-energy physics. Our aim is to minimise energy ...
Parallel computing with task scheduling. Contribute to dask/dask development by creating an account on GitHub.
Cao. An online parallel scheduling method with application to energy-efficiency in cloud computing. J. Supercomput., 66(3):1773-1790, Dec. 2013.Tian, Wenhong, Qin Xiong, and Jun Cao(2013)." An online parallel scheduling method with application to energy-efficiency in cloud computing.& quot...
It can be seen that other factors such as memory, hard disk, and network interface have a very small impact on total power consumption. In Ref. [19], authors find that CPU utilization is typically proportional to the overall system load and propose a power model defined in Eq. (6.2): ...
Azure Batch enables the execution of large-scale parallel computing tasks in the cloud, making it ideal for HPC workloads.Figure 7: Azure Batch architecture 6 Pros: Scalability: According to G2 reviews, Azure Batch allows users to frequently run a variety of tasks and process job batches in...
Second, to optimize system throughput, PAR-BS employs a parallelism-aware DRAM scheduling policy that aims to process requests from a thread in parallel in the DRAM banks, thereby reducing the memory-related stall-time experienced by the thread. PAR-BS seamlessly incorporates ...
In the shared data center environment, Nagendram et al. have depicted the resource scheduling problem to a bounded multidimensional knapsack problem, taking into account the requirement dependency among multidimensional resources including memory, storage, CPU, and network bandwidth. Then, they have prese...
In this paper we present an efficient algorithm for compile-time scheduling and clustering of parallel programs onto parallel processing systems with distributed memory, which is called The Dynamic Critical Path Scheduling DCPS. The DCPS is superior to several other algorithms from the literature in te...
We introduce a new parallelization framework for scientific computing based on BDSC, an efficient automatic scheduling algorithm for parallel programs in the presence of resource constraints on the number of processors and their local memory size. BDSC extends Yang and Gerasoulis’s Dominant Sequence Cl...