Design and Implementation of Performance Counters for Real Time Database Server Clients

The aim of this research is to monitor, check, and do the necessaryrefinements for the performance of clients working with Database server in thereal time environment. If there are a number of clients in the network need toaccess Database server designed for distributed real time system in particularsequence, this will cause many bottlenecks in the system, making the work of thesystem unstable, especially in critical systems such as that of power and waterdistribution. A designed performance counters and objects were added to eachclient to know who makes the bottlenecks, also they will be used while developingand debugging the clients when they access the Database server on the network inorder to tune the performance of the system. After completing the designed systemand installing it at the target, the counters can help system administrators to adjustconfigurable settings for that system. Using this new performance counters in theexecution time will help to see the effect of clients on each other, on network, andon performance of the Database server. The results show that, the designedperformance counters can detect the bottlenecks which are caused by week pointsin Client’s program code, so they will help the programmers to amendment andredistribute the client’s program code on the network with minimum errors whenaccessing DB server.Windows NT/XP/2000 provides a mechanism for developersto add performance objects and counters for their applications and other softwarecomponents. These objects and counters can provide performance data toWindows NT/XP/2000 Performance Monitor

Measuring and Analyzing Energy Consumption of the Data Center

Data centers are continuously expanding, so does the energy consumed to power their infrastructure. Server is the major component of data center’s computer rooms, which runs the most intensive computational workloads and stores the data. Server is responsible for more than a quarter of the total energy consumption of data center. This thesis is focused on analyzing and predicting the energy consumption of the server. Three major components are considered in our study; the processor, the access memory and the network interface controller. We collect data from these components and analyze them using linear regression Lasso model with non-negative coefficients. A power model is proposed for predicting energy consumption at the system-level. The model takes as input CPU cycles and data Translation Lookaside Buffer loads, and predicts the energy consumption of the server with 5.33% median error regardless of its workload

Balancing Power Consumption in Multiprocessor Systems

Actions usually taken to prevent processors from overheating, such as decreasing the frequency or stopping the execution flow, also degrade performance. Multiprocessor systems, however, offer the possibility of moving the task that caused a CPU to overheat away to some other, cooler CPU, so throttling becomes only a last resort taken if all of a system\u27s processors are hot. Additionally, the scheduler can take advantage of the energy characteristics of individual tasks, and distribute hot tasks as well as cool tasks evenly among all CPUs. This work presents a mechanism for determining the energy characteristics of tasks by means of event monitoring counters, and an energy-aware scheduling policy that strives to assign tasks to CPUs in a way that avoids overheating individual CPUs. Our evaluations show that the benefit of avoiding throttling outweighs the overhead of additional task migrations, and that energy-aware scheduling in many cases increases the system\u27s throughput

Predictive power management for multi-core processors

textEnergy consumption by computing systems is rapidly increasing due to the growth of data centers and pervasive computing. In 2006 data center energy usage in the United States reached 61 billion kilowatt-hours (KWh) at an annual cost of 4.5 billion USD [Pl08]. It is projected to reach 100 billion KWh by 2011 at a cost of 7.4 billion USD. The nature of energy usage in these systems provides an opportunity to reduce consumption. Specifically, the power and performance demand of computing systems vary widely in time and across workloads. This has led to the design of dynamically adaptive or power managed systems. At runtime, these systems can be reconfigured to provide optimal performance and power capacity to match workload demand. This causes the system to frequently be over or under provisioned. Similarly, the power demand of the system is difficult to account for. The aggregate power consumption of a system is composed of many heterogeneous systems, each with a unique power consumption characteristic. This research addresses the problem of when to apply dynamic power management in multi-core processors by accounting for and predicting power and performance demand at the core-level. By tracking performance events at the processor core or thread-level, power consumption can be accounted for at each of the major components of the computing system through empirical, power models. This also provides accounting for individual components within a shared resource such as a power plane or top-level cache. This view of the system exposes the fundamental performance and power phase behavior, thus making prediction possible. This dissertation also presents an extensive analysis of complete system power accounting for systems and workloads ranging from servers to desktops and laptops. The analysis leads to the development of a simple, effective prediction scheme for controlling power adaptations. The proposed Periodic Power Phase Predictor (PPPP) identifies patterns of activity in multi-core systems and predicts transitions between activity levels. This predictor is shown to increase performance and reduce power consumption compared to reactive, commercial power management schemes by achieving higher average frequency in active phases and lower average frequency in idle phases.Electrical and Computer Engineerin

Using Performance Counters for Runtime Temperature Sensing in High-Performance Processors

As energy consumption in high-performance systems has increased, thermal management has become a big challenge. Providing a cost-effective and detailed temperature sensing mechanism is crucial to effectively employ a thermal management technique. Existing hardware sensors are too costly to implement and add additional heat while software simulations fail to account for all possible hardware effects. In this paper, we describe a software solution for temperature sensing that uses real hardware resources such as performance counters. The resulting temperature model provides a detailed spatial gradient of the processor and executes at runtime. In particular, the model is configured for the Pentium 4 processor. We run SPEC2000 benchmarks to analyze the thermal behavior of applications and explain the potential benefits of using our model for temperatureaware research. 1

Using Performance Counters for Runtime Temperature Sensing in High-Performance Processors

As energy consumption in high-performance systems has increased, thermal management has become a big challenge. Providing a cost-effective and detailed temperature sensing mechanism is crucial to effectively employ a thermal management technique. Existing hardware sensors are too costly to implement and add additional heat while software simulations fail to account for all possible hardware effects. In this paper, we describe a software solution for temperature sensing that uses real hardware resources such as performance counters. The resulting temperature model provides a detailed spatial gradient of the processor and executes at runtime. In particular, the model is configured for the Pentium 4 processor. We run SPEC2000 benchmarks to analyze the thermal behavior of applications and explain the potential benefits of using our model for temperatureaware research. 1

Using Performance Counters for Runtime Temperature Sensing in High-Performance Processors

As energy consumption in high-performance systems has increased, thermal management has become a big challenge. Providing a cost-effective and detailed temperature sensing mechanism is crucial to effectively employ a thermal management technique. Existing hardware sensors are too costly to implement and add additional heat while software simulations fail to account for all possible hardware effects. In this paper, we describe a software solution for temperature sensing that uses real hardware resources such as performance counters. The resulting temperature model provides a detailed spatial gradient of the processor and executes at runtime. In particular, the model is configured for the Pentium 4 processor. We run SPEC2000 benchmarks to analyze the thermal behavior of applications and explain the potential benefits of using our model for temperatureaware research. 1

Task Activity Vectors: A Novel Metric for Temperature-Aware and Energy-Efficient Scheduling

This thesis introduces the abstraction of the task activity vector to characterize applications by the processor resources they utilize. Based on activity vectors, the thesis introduces scheduling policies for improving the temperature distribution on the processor chip and for increasing energy efficiency by reducing the contention for shared resources of multicore and multithreaded processors