Compiler and runtime support for predictive control of power and cooling

The low cost of clusters built using commodity compo-nents has made it possible for many more users to purchase their own supercomputer. However, even modest-sized clus-ters make significant demands on the power and cooling in-frastructure. Minimizing impact of problems after they are detected is not as effective as avoiding problems altogether. This paper is about achieving the best system performance by predicting and avoiding power and cooling problems. Although measuring power and thermal properties of a code is not trivial, the primary issue is making predictions sufficiently in advance so that they can be used to drive pre-dictive, rather than just reactive, control at runtime. This paper presents new compiler analysis supporting interpro-cedural power prediction and a variety of other compiler and runtime technologies making feed-forward control fea-sible. The techniques apply to most computer systems, but some properties specific to clusters and parallel supercom-puting are used where appropriate. 1

E2DR: Energy Efficient Data Replication in Data Grid

Abstract— Data grids are an important branch of gird computing which provide mechanisms for the management of large volumes of distributed data. Energy efficiency has recently emerged as a hot topic in large distributed systems. The development of computing systems is traditionally focused on performance improvements driven by the demand of client's applications in scientific and business domains. High energy consumption in computer systems leads to their limited performance because of the increased consumption of carbon dioxide and amount of electricity bills. Thus, the goal of design of computer systems has been shifted to power and energy efficiency. Data grids can solve large scale applications that require a large amount of data. Data replication is a common solution to improve availability and file access time in such environments. This solution replicates the data file in many different sites. In this paper, a new data replication method is proposed that is not only data aware, but also is energy efficient. Simulation results with CLOUDSIM show that the proposed method gives better energy consumption, average response time, and network usage than other algorithms and prevents the unnecessary creation of replica, which leads to efficient storage usage

EXTRACTION AND PREDICTION OF SYSTEM PROPERTIES USING VARIABLE-N-GRAM MODELING AND COMPRESSIVE HASHING

In modern computer systems, memory accesses and power management are the two major performance limiting factors. Accesses to main memory are very slow when compared to operations within a processor chip. Hardware write buffers, caches, out-of-order execution, and prefetch logic, are commonly used to reduce the time spent waiting for main memory accesses. Compiler loop interchange and data layout transformations also can help. Unfortunately, large data structures often have access patterns for which none of the standard approaches are useful. Using smaller data structures can significantly improve performance by allowing the data to reside in higher levels of the memory hierarchy. This dissertation proposes using lossy data compression technology called ’Compressive Hashing’ to create “surrogates”, that can augment original large data structures to yield faster typical data access. One way to optimize system performance for power consumption is to provide a predictive control of system-level energy use. This dissertation creates a novel instruction-level cost model called the variable-n-gram model, which is closely related to N-Gram analysis commonly used in computational linguistics. This model does not require direct knowledge of complex architectural details, and is capable of determining performance relationships between instructions from an execution trace. Experimental measurements are used to derive a context-sensitive model for performance of each type of instruction in the context of an N-instruction sequence. Dynamic runtime power prediction mechanisms often suffer from high overhead costs. To reduce the overhead, this dissertation encodes the static instruction-level predictions into a data structure and uses compressive hashing to provide on-demand runtime access to those predictions. Genetic programming is used to evolve compressive hash functions and performance analysis of applications shows that, runtime access overhead can be reduced by a factor of ~3x-9x

Compiler And Runtime Support For Predictive Control Of Power And Cooling

Abstract — The low cost of clusters built using commodity components has made it possible for many more users to purchase their own supercomputer. However, even modestsized clusters make significant demands on the power and cooling infrastructure. Minimizing the impact of problems after they are detected is not as effective as avoiding problems altogether. This paper is about achieving the best system performance by predicting and avoiding power and cooling problems. Although measuring power and thermal properties of a code is not trivial, the primary issue is making predictions sufficiently in advance so that they can be used to drive predictive, rather than just reactive, control at runtime. This paper presents new compiler analysis supporting interprocedural power prediction and a variety of other compiler and runtime technologies making feed-forward control feasible. The techniques apply to most computer systems, but some properties specific to a clusters and parallel supercomputing are used where appropriate. I