Energy Wall for Exascale Supercomputing

"Sustainable development" is one of the major issues in the 21st century. Thus the notions of green computing, green development and so on show up one after another. As the large-scale parallel computing systems develop rapidly, energy consumption of such systems is becoming very huge, especially system performance reaches Petascale (10^15 Flops) or even Exascale (10^18 Flops). The huge energy consumption increases the system temperature, which seriously undermines the stability and reliability, and limits the growth of system size. The effects of energy consumption on scalability become a growing concern. Against the background, this paper proposes the concept of "Energy Wall" to highlight the significance of achieving scalable performance in peta/exascale supercomputing by taking energy consumption into account. We quantify the effect of energy consumption on scalability by building the energy-efficiency speedup model, which integrates computing performance and system energy. We define the energy wall quantitatively, and provide the theorem on the existence of the energy wall, and categorize the large-scale parallel computers according to the energy consumption. In the context of several representative types of HPC applications, we analyze and extrapolate the existence of the energy wall considering three kinds of topologies, 3D-Torus, binary n-cube and Fat tree which provides insights on how to mitigate the energy wall effect in system design and through hardware/software optimization in peta/exascale supercomputing

Using dynamic compilation for continuing execution under reduced memory availability

This paper explores the use of dynamic compilation for continuing execution even if one or more of the memory banks used by an application become temporarily unavailable (but their contents are preserved), that is, the number of memory banks available to the application varies at runtime. We implemented the proposed dynamic compilation approach using a code instrumentation system and performed experiments with 12 embedded benchmark codes. The results collected so far are very encouraging and indicate that, even when all the overheads incurred by dynamic compilation are included, the proposed approach still brings significant benefits over an alternate approach that suspends application execution when there is a reduction in memory bank availability and resumes later when all the banks are up and running. © 2009 EDAA

A Study of Adaptation Mechanisms for Simulation Algorithms

The performance of a program can sometimes greatly improve if it was known in advance the features of the input the program is supposed to process, the actual operating parameters it is supposed to work with, or the specific environment it is to run on. However, this information is typically not available until too late in the program’s operation to take advantage of it. This is especially true for simulation algorithms, which are sensitive to this late-arriving information, and whose role in the solution of decision-making, inference and valuation problems is crucial. To overcome this limitation we need to provide the flexibility for a program to adapt its behaviour to late-arriving information once it becomes available. In this thesis, I study three adaptation mechanisms: run-time code generation, model-specific (quasi) Monte Carlo sampling and dynamic computation offloading, and evaluate their benefits on Monte Carlo algorithms. First, run-time code generation is studied in the context of Monte Carlo algorithms for time-series filtering in the form of the Input-Adaptive Kalman filter, a dynamically generated state estimator for non-linear, non-Gaussian dynamic systems. The second adaptation mechanism consists of the application of the functional-ANOVA decomposition to generate model-specific QMC-samplers which can then be used to improve Monte Carlo-based integration. The third adaptive mechanism treated here, dynamic computation offloading, is applied to wireless communication management, where network conditions are assessed via option valuation techniques to determine whether a program should offload computations or carry them out locally in order to achieve higher run-time (and correspondingly battery-usage) efficiency. This ability makes the program well suited for operation in mobile environments. At their core, all these applications carry out or make use of (quasi) Monte Carlo simulations on dynamic Bayesian networks (DBNs). The DBN formalism and its associated simulation-based algorithms are of great value in the solution to problems with a large uncertainty component. This characteristic makes adaptation techniques like those studied here likely to gain relevance in a world where computers are endowed with perception capabilities and are expected to deal with an ever-increasing stream of sensor and time-series data