Data-driven spatial locality

Over the past decades, core speeds have been improving at a much higher rate than memory bandwidth. This has caused the performance bottlenecks in modern software to shift from computation to data transfers. Hardware caches were designed to mitigate this problem, based on the principles of temporal and spatial locality. However, with the increasingly irregular access patterns in software, locality is difficult to preserve. Researchers and practitioners devote a lot of time and effort to improving memory performance from the software side. This is done either by restructuring the code to make access patterns more regular, or by changing the layout of data in memory to better accommodate caching policies. Experts often use correlations between the access pattern of an algorithm and properties of the objects it operates on to devise new ways to lay data out in memory. Prior work has shown the memory layout design process to be largely manual and difficult enough to result in high level publications. Our contribution is a set of tools, techniques and algorithms for automatic extraction of correlations between data and access patterns of programs. In order to collect a sufficient level of details about memory accesses, we present a compiler-based access instrumentation framework called DINAMITE. Further, we introduce access graphs, a novel way of representing spatial locality properties of programs which are generated from memory access traces. We use access graphs as a basis for Hierarchical Memory Layouts -- a novel algorithm for estimating performance improvements to be gained from better data layouts. Finally, we present our Data-Driven Spatial Locality techniques which use the information available from previous steps to automatically extract the correlations between data and access patterns commonly used by experts to inform better layout design.Applied Science, Faculty ofElectrical and Computer Engineering, Department ofGraduat

Accelerating resource allocation in virtualized environments using workload classes and/or workload signatures

Systems, methods, and apparatus for managing resources assigned to an application or service. A resource manager maintains a set of workload classes and classifies workloads using workload signatures. In specific embodiments, the resource manager minimizes or reduces resource management costs by identifying a relatively small set of workload classes during a learning phase, determining preferred resource allocations for each workload class, and then during a monitoring phase, classifying workloads and allocating resources based on the preferred resource allocation for the classified workload. In some embodiments, interference is accounted for by estimating and using an "interference index"

DejaVu : Accelerating Resource Allocation in Virtualized Environments

Effective resource management of virtualized environments is a challenging task. State-of-the-art management systems either rely on analytical models or evaluate resource allocations by running actual experiments. However, both approaches incur a significant overhead once the workload changes. The former needs to recalibrate and re-validate models, whereas the latter has to run a new set of experiments to select a new resource allocation. During the adaptation period, the system may run with an inefficient configuration.Qc 20140707</p

DejaVu : Accelerating Resource Allocation in Virtualized Environments

Effective resource management of virtualized environments is a challenging task. State-of-the-art management systems either rely on analytical models or evaluate resource allocations by running actual experiments. However, both approaches incur a significant overhead once the workload changes. The former needs to recalibrate and re-validate models, whereas the latter has to run a new set of experiments to select a new resource allocation. During the adaptation period, the system may run with an inefficient configuration.Qc 20140707</p