19,996 research outputs found
Modeling and visualizing networked multi-core embedded software energy consumption
In this report we present a network-level multi-core energy model and a
software development process workflow that allows software developers to
estimate the energy consumption of multi-core embedded programs. This work
focuses on a high performance, cache-less and timing predictable embedded
processor architecture, XS1. Prior modelling work is improved to increase
accuracy, then extended to be parametric with respect to voltage and frequency
scaling (VFS) and then integrated into a larger scale model of a network of
interconnected cores. The modelling is supported by enhancements to an open
source instruction set simulator to provide the first network timing aware
simulations of the target architecture. Simulation based modelling techniques
are combined with methods of results presentation to demonstrate how such work
can be integrated into a software developer's workflow, enabling the developer
to make informed, energy aware coding decisions. A set of single-,
multi-threaded and multi-core benchmarks are used to exercise and evaluate the
models and provide use case examples for how results can be presented and
interpreted. The models all yield accuracy within an average +/-5 % error
margin
PROFET: modeling system performance and energy without simulating the CPU
The approaching end of DRAM scaling and expansion of emerging memory technologies is motivating a lot of research in future memory systems. Novel memory systems are typically explored by hardware simulators that are slow and often have a simplified or obsolete abstraction of the CPU. This study presents PROFET, an analytical model that predicts how an application's performance and energy consumption changes when it is executed on different memory systems. The model is based on instrumentation of an application execution on actual hardware, so it already takes into account CPU microarchitectural details such as the data prefetcher and out-of-order engine. PROFET is evaluated on two real platforms: Sandy Bridge-EP E5-2670 and Knights Landing Xeon Phi platforms with various memory configurations. The evaluation results show that PROFET's predictions are accurate, typically with only 2% difference from the values measured on actual hardware. We release the PROFET source code and all input data required for memory system and application profiling. The released package can be seamlessly installed and used on high-end Intel platforms.Peer ReviewedPostprint (author's final draft
Formal and Informal Methods for Multi-Core Design Space Exploration
We propose a tool-supported methodology for design-space exploration for
embedded systems. It provides means to define high-level models of applications
and multi-processor architectures and evaluate the performance of different
deployment (mapping, scheduling) strategies while taking uncertainty into
account. We argue that this extension of the scope of formal verification is
important for the viability of the domain.Comment: In Proceedings QAPL 2014, arXiv:1406.156
DReAM: An approach to estimate per-Task DRAM energy in multicore systems
Accurate per-task energy estimation in multicore systems would allow performing per-task energy-aware task scheduling and energy-aware billing in data centers, among other applications. Per-task energy estimation is challenged by the interaction between tasks in shared resources, which impacts tasks’ energy consumption in uncontrolled ways. Some accurate mechanisms have been devised recently to estimate per-task energy consumed on-chip in multicores, but there is a lack of such mechanisms for DRAM memories. This article makes the case for accurate per-task DRAM energy metering in multicores, which opens new paths to energy/performance optimizations. In particular, the contributions of this article are (i) an ideal per-task energy metering model for DRAM memories; (ii) DReAM, an accurate yet low cost implementation of the ideal model (less than 5% accuracy error when 16 tasks share memory); and (iii) a comparison with standard methods (even distribution and access-count based) proving that DReAM is much more accurate than these other methods.Peer ReviewedPostprint (author's final draft
Using fast and accurate simulation to explore hardware/software trade-offs in the multi-core era
Writing well-performing parallel programs is challenging in the multi-core processor era. In addition to achieving good per-thread performance, which in itself is a balancing act between instruction-level parallelism, pipeline effects and good memory performance, multi-threaded programs complicate matters even further. These programs require synchronization, and are affected by the interactions between threads through sharing of both processor resources and the cache hierarchy.
At the Intel Exascience Lab, we are developing an architectural simulator called Sniper for simulating future exascale-era multi-core processors. Its goal is twofold: Sniper should assist hardware designers to make design decisions, while simultaneously providing software designers with a tool to gain insight into the behavior of their algorithms and allow for optimization. By taking architectural features into account, our simulator can provide more insight into parallel programs than what can be obtained from existing performance analysis tools. This unique combination of hardware simulator and software performance analysis tool makes Sniper a useful tool for a simultaneous exploration of the hardware and software design space for future high-performance multi-core systems
Computationally-intensive Econometrics using a Distributed Matrix-programming Language
This paper reviews the need for powerful facilities in econometrics, focusing on concrete problems which arise in financial economics and in macroeconomics. We argue that the profession is being held back by the lack of easy to use generic software which is able to exploit the availability of cheap clusters of distributed computers. Our response is to extend, in a number of directions, the well known matrix-programming interpreted language Ox developed by the first author. We note three possible levels of extensions: (i) Ox with parallelization explicit in the Ox code; (ii) Ox with a parallelized run-time library; (iii) Ox with a parallelized interpreter. This paper studies and implements the first case, emphasizing the need for deterministic computing in science. We give examples in the context of financial economics and time-series modelling.Distributed computing; Econometrics; High-performance computing; Matrix-programming language
Experimental analysis of computer system dependability
This paper reviews an area which has evolved over the past 15 years: experimental analysis of computer system dependability. Methodologies and advances are discussed for three basic approaches used in the area: simulated fault injection, physical fault injection, and measurement-based analysis. The three approaches are suited, respectively, to dependability evaluation in the three phases of a system's life: design phase, prototype phase, and operational phase. Before the discussion of these phases, several statistical techniques used in the area are introduced. For each phase, a classification of research methods or study topics is outlined, followed by discussion of these methods or topics as well as representative studies. The statistical techniques introduced include the estimation of parameters and confidence intervals, probability distribution characterization, and several multivariate analysis methods. Importance sampling, a statistical technique used to accelerate Monte Carlo simulation, is also introduced. The discussion of simulated fault injection covers electrical-level, logic-level, and function-level fault injection methods as well as representative simulation environments such as FOCUS and DEPEND. The discussion of physical fault injection covers hardware, software, and radiation fault injection methods as well as several software and hybrid tools including FIAT, FERARI, HYBRID, and FINE. The discussion of measurement-based analysis covers measurement and data processing techniques, basic error characterization, dependency analysis, Markov reward modeling, software-dependability, and fault diagnosis. The discussion involves several important issues studies in the area, including fault models, fast simulation techniques, workload/failure dependency, correlated failures, and software fault tolerance
- …