that make heavy use of the CPU with gangs that make light use of the CPU (presumably due to I/O or communication activity), and schedule such pairs together, allowing the local scheduler on each node to select either of the two processes at any instant. As I/O-intensive gangs make light use of the CPU

. Jobs that do not require all processors in the system can be packed together for gang scheduling. We examine accounting traces from several parallel computers to show that indeed many jobs have small sizes and can be packed together. We then formulate a number of such packing algorithms

Abstract Multiprogrammed multiprocessors executing fine-grained parallel programs appear to require new scheduling policies. A promising new idea is gang scheduling, where a set of threads are scheduled to execute simultaneously on a set of processors. This has the intuitive appeal of supplying

? This dillema is solved by using gang scheduling, because then the impact of each decision is limited to its time slice, and future arrivals can be accommodated in other time slices. This added flexibility y is shown to improve overall system utilization and responsiveness. Empirical evidence from using gang

Today’s data centers offer tremendous aggregate bandwidth to clusters of tens of thousands of machines. However, because of limited port densities in even the highest-end switches, data center topologies typically consist of multi-rooted trees with many equal-cost paths between any given pair

A major problem with time slicing on parallel machines is memory pressure, as the resulting paging activity damages the synchronism among a job’s processes. An alternative is to impose admission controls, and only admit jobs that fit into the available memory. Despite suffering from delayed execution, this leads to better overall performance by preventing the harmful effects of paging and thrashing. 1.

In this paper, we propose a static scheduling algorithm for allocating task graphs to fullyconnected multiprocessors. We discuss six recently reported scheduling algorithms and show that they possess one drawback or the other which can lead to poor performance. The proposed algorithm, which

The generalized assignment problem can be viewed as the following problem of scheduling parallel machines with costs. Each job is to be processed by exactly one machine; processing job j on machine i requires time pif and incurs a cost of c,f, each machine / is available for 7", time units

Using a single traditional gang scheduling algorithm cannot provide the best performance for all workloads and parallel architectures. A solution for this problem is the use of an algorithm that is capable of dynamically changing its form (configuration) into a more appropriate one, according

We define the bounds of two gang service algorithms, namely time balanced and instruction balanced, using interactivity and throughput based metrics. The workload is composed of V-RAM jobs, and the architectural model chosen is LogP. We also analyze the effect of workload and space sharing