14888784
Integrated Data Reorganization and Disk Mapping for Reducing Disk Energy Consumption
Increasing power consumption of high-performance systems leads to reliability, survivability, and cooling related problems. Motivated by this observation, several recent efforts focused on reducing disk power consumption through hardware, OS and compiler based techniques. This paper presents a novel approach to reducing disk power consumption of large-scale, array-intensive scientific applications. It proposes and evaluates a compiler-based approach that employs two complementary techniques: data reorganization and disk mapping. The first of these, data reorganization, determines a suitable layout for data in the array space, whereas the second technique, disk mapping, decides the corresponding layout in the disk space. The goal of data reorganization and disk mapping is to ensure that data (from the different disk-resident arrays) that are accessed within the same loop iteration are colocated in the same set of disks. In this way, we can increase disk inter-access times (idle periods of disks) and this in turn allows better exploitation of the underlying hardware mechanisms used for reducing power. Our experiments with eight disk I/O-intensive scientific applications indicate that the proposed approach brings significant reductions in energy consumption, whether the underlying disk system uses spin-down disks or speed-reduced disks, two previously- proposed hardware-based disk power reduction schemes. The results also show that both the components of our scheme (data reorganization and disk mapping) are very important since applying any of these components alone does not generate large savings for most of our applica tions.
2552667
An evaluation of code and data optimizations in the context of disk power reduction
Disk power management is becoming increasingly important in high-end server and cluster type of environments that execute data-intensive applications. While hardware-only approaches (e.g., low-power modes supported by current disks) are successful to a certain extent, one also needs to consider the software side to achieve further energy savings. This paper first demonstrates that conventional data locality oriented code transformations are not sufficient for minimizing disk power consumption. The reason is that these optimizations do not take into account how disk-resident array data are laid out on the disk system, and consequently, fail to increase idle periods of disks, which is the primary metric using which disk power can be reduced. Instead, we propose a disk layout aware application optimization strategy that uses both code restructuring and data layout optimization. Our experimental evaluation with several benchmark codes reveal that the proposed strategy is very successful in reducing disk energy consumption without performing much worse than a pure data locality oriented scheme, as far as execution cycles are concerned. The experiments also show that the benefits coming from our approach increase with the increased number of disks; i.e., it scales very well.
1458308
Memory Bank Aware Dynamic Loop Scheduling
In a parallel system with multiple CPUs, one of the key problems is to assign loop iterations to processors. This problem, known as the loop scheduling problem, has been studied in the past, and several schemes, both static and dynamic, have been proposed. One of the attractive features of dynamic schemes, as compared to their static counterparts, is their ability of exploiting the latency variations across the execution times of the different loop iterations. In all the dynamic loop scheduling techniques proposed in literature so far, performance has been the primary metric of interest. In a battery-operated embedded execution environment, however, power consumption is another metric to consider during iteration-to-processor assignment. In particular, in a banked memory system, this assignment can have an important impact on memory power consumption, which can be a significant portion of the overall energy consumption, especially for data-intensive embedded applications such as those from the domain of image data processing. This paper presents a bank aware dynamic loop scheduling scheme for array-intensive embedded media applications. The goal behind this new scheduling scheme is to minimize the number of memory banks that need to be used for executing the current working set (group of loop iterations) when all processors are considered together. That is, during the loop iteration-to-processor assignment, our approach considers the bank access patterns of loop iterations and carefully selects the set of iterations to assign to an idle processor so that, if possible, the number of memory banks that are used at the current state is not increased. Our experimental results show that the proposed scheduling scheme leads to much better energy results when compared to prior loop scheduling techniques and it is also competitive with the scheduler that generates the best performance. To our knowledge, this is the first dynamic loop scheduling scheme that is memory bank aware.
1429882
Reducing Power through Compiler-Directed Barrier Synchronization Elimination
Interprocessor synchronization, while extremely important for ensuring execution correctness, can be very costly in terms of both power and performance overheads. Unfortunately, many parallelizing compilers are very conservative in inserting barrier synchronizations at the end of each and every parallel loop. This can lead to significant power consumption in chip multiprocessor based execution environments. This paper proposes a compiler-directed approach for eliminating such synchronization calls between neighboring parallel loops. It achieves its goal by partitioning loop iterations across processors such that each processor executes iterations from both the loops that access the same set of array elements. We implemented the proposed approach using an experimental compilation framework and made experiments with ten SPEC benchmark codes. Our experiments clearly show that the proposed compiler-directed approach is very effective and reduces energy overheads due to synchronizations by about 75.5%, and this corresponds to around 5.48% saving on average in overall energy consumption
8993751
A compiler-guided approach for reducing disk power consumption by exploiting disk access locality
Power consumption of large servers and clusters has recently been a popular research topic, since this issue is important from both technical and environmental viewpoints. The prior research proposed disk power management as one of the important ways of reducing overall power of a large system and considered both hardware-based and software-guided disk power reduction schemes. One of the common characteristics of the previously proposed approaches to disk power reduction is that they work with a given disk access pattern. In comparison, the goal of the approach proposed in this paper is to restructure application code using an optimizing compiler so that disk idle periods are lengthened. This in turn allows the underlying disk power management scheme to be more effective since such schemes usually prefer the long idle periods over the short ones. Our approach targets at large scientific applications that operate on disk-resident arrays using nested loops and exhibit regular data access patterns. To test the effectiveness of the proposed approach, we implemented it within an optimizing compiler and performed experiments with six data-intensive applications that manipulate disk-resident data. Our experimental analysis shows that the proposed approach is very successful in practice and reduces the total disk energy consumption on average by 18.17%, as compared to an execution without any disk power management, and by 11.55%, as compared to an execution that employs disks with low-power capabilities without our code restructuring approach.
11209063
Integrated link/CPU voltage scaling for reducing energy consumption of parallel sparse matrix applications
Reducing power consumption is quickly becoming a first-class optimization metric for many high-performance parallel computing platforms. One of the techniques employed by many prior proposals along this direction is voltage scaling and past research used it on different components such as networks, CPUs, and memories. In contrast to most of the existent efforts on voltage scaling that target a single component (CPU, network or memory components), this paper proposes and experimentally evaluates a voltage/frequency scaling algorithm that considers CPU and communication links in a mesh network at the same time. More specifically, it scales voltages/frequencies of both CPUs in the network and the communication links among them in a coordinated fashion (instead of one after another) such that energy savings are maximized without impacting execution time. Our experiments with several tree-based sparse matrix computations reveal that the proposed integrated voltage scaling approach is very effective in practice and brings 13% and 17% energy savings over the pure CPU and pure communication link voltage scaling schemes, respectively. The results also show that our savings are consistent with the different network sizes and different sets of voltage/frequency levels
1721718
Recovering internet symmetry in distributed computing
This paper describes two systems to recover the Internet connectivity impaired by private networks and firewalls. These devices cause asymmetry in the Internet, making peer-to-peer computing difficult or even impossible. The Condor system is one of those that are severely impaired by the asymmetry. Compared to normal peer-to-peer computing applications, Condor has stricter requirements, which are representative to any grid computing. To make Condor seamlessly work across private networks and over firewalls, we designed and implemented Dynamic Port Forwarding (DPF) and Generic Connection Brokering (GCB). Both DPF and GCB satisfy the representative requirements. Furthermore DPF supports dedicated large clusters very well because it is simple, efficient, and highly scalable. On the other hand, GCB perfectly supports non-dedicated or personal clusters because it is independent to private network or firewall technologies and does not require airy administrative power to deploy it. In this paper, we describe the implementations of DPF and GCB and analyze them with respect to performance, deployability, security, and scalability.
9316311
Managing deadline miss ratio and sensor data freshness in real-time databases
The demand for real-time data services is increasing in many applications including e-commerce, agile manufacturing, and telecommunications network management. In these applications, it is desirable to execute transactions within their deadlines, i.e., before the real-world status changes, using fresh (temporally consistent) data. However, meeting these fundamental requirements is challenging due to dynamic workloads and data access patterns in these applications. Further, transaction timeliness and data freshness requirements may conflict. We define average/transient deadline miss ratio and new data freshness metrics to let a database administrator specify the desired quality of real-time data services for a specific application. We also present a novel QoS management architecture for real-time databases to support the desired QoS even in the presence of unpredictable workloads and access patterns. To prevent overload and support the desired QoS, the presented architecture applies feedback control, admission control, and flexible freshness management schemes. A simulation study shows that our QoS-aware approach can achieve a near zero miss ratio and perfect freshness, meeting basic requirements for real-time transaction processing. In contrast, baseline approaches fail to support the desired miss ratio and/or freshness in the presence of unpredictable workloads and data access patterns.
6149276
Real-Time Databases and Data Services
Typically, a real–time system consists of a a controlling system and a controlled system. In an automated factory, the controlled system is the factory floor with its robots, assembling stations, and the assembled parts, while the controlling system is the computer and human interfaces that manage and coordinate the activities on the factory floor. Thus, the controlled system can be viewed as the environment with which the computer interacts. The controlling system interacts with its environment based on the data available about the environment, say from various sensors, e.g. temperature and pressure sensors. It is imperative that the state of the environment, as perceived by the controlling system, be consistent with the actual state of the environment. Otherwise, the effects of the controlling systems’ activities may be disastrous. Hence, timely monitoring of the environment as well as timely processing of the sensed information is necessary. The sensed data is processed further to derive new data. For example, the temperature and pressure information pertaining to a reaction may be used to derive the rate at which the reaction appears to be progressing. This derivation typically would depend on past temperature and pressure trends and so some of the needed information may have to be fetched from archival storage. Based on the derived data, where the derivation may involve multiple steps, actuator commands are set. For instance, in our example, the derived reaction rate is used to determine the amount of chemicals or coolant to be added to the reaction. In general, the history of (interactions with) the environment are also logged in archival storage. In addition to the timing constraints that arise from the need to continuously track the environment, timing correctness requirements in a real–time (database) system also arise because of the need to make data available to the controlling system for its decision-making activities. If the computer controlling a robot does not command it to stop or turn on time, the robot might collide with another object on the factory floor. Needless to say, such a mishap can result in a major catastrophe. Besides robotics, applications such as medical patient monitoring, programmed stock trading, and military command and control systems like submarine contact tracking require timely actions as well as the ability to access and store complex data that reflects the state of the application’s environment. That is, data in these applications must be valid, or fresh, when it is accessed in order for the application to perform correctly. In a patient monitoring system, data such as heart rate, temperature, and blood pressure must be collected periodically. Transactions that monitor the danger level of a patient’s status must be performed within a specified time, and the data must be accessed within an interval that defines the validity of the data. If not, the computations made by the transactions do not reflect the current state of the patient’s health. A traditional database provides some of the functionality required by these applications, such as coordination of concurrent actions and consistent access to shared data. But they do not provide for enforcement of the
2318893
The Kangaroo approach to data movement on the Grid
Access to remote data is one of the principal challenges of Grid computing. While performing I/O, Grid applications must be prepared for server crashes, performance variations and exhausted resources. To achieve high throughput in such a hostile environment, applications need a resilient service that moves data while hiding errors and latencies. We illustrate this idea with Kangaroo, a simple data movement system that makes opportunistic use of disks and networks to keep applications running. We demonstrate that Kangaroo can achieve better end-to-end performance than traditional data movement techniques, even though its individual components do not achieve high performance.
30591708
Infrastrukturentscheidungen in vernetzten Unternehmen Eine Wirtschaftlichkeitsanalyse am Beispiel von X.500 Directory Services
To support decisions concerning the planning and implementation of communication infrastructures between six German banking and insurance enterprises, costs and benefits of IT-standards are identified and data are empirically collected, taking X.500-based Directory Services as an example. Potential benefits amount to DM 24 million equivalent to a return on investment of 1100%.