Massoud Pedram

Bio: Massoud Pedram is an academic researcher from University of Southern California. The author has contributed to research in topics: Energy consumption & CMOS. The author has an hindex of 77, co-authored 780 publications receiving 23047 citations. Previous affiliations of Massoud Pedram include University of California, Berkeley & Syracuse University.

A Comparative Study of the Effectiveness of CPU Consolidation Versus Dynamic Voltage and Frequency Scaling in a Virtualized Multicore Server

Abstract: Companies operating large datacenters are focusing on how to reduce the electrical energy costs of operating datacenters. A common way of cost reduction is to perform a dynamic voltage and frequency scaling (DVFS), thereby matching the CPU’s performance and power level to incoming workloads. Another power saving technique is CPU consolidation, which uses the minimum number of CPUs necessary to meet the service request demands and turns OFF the remaining unused CPUs. DVFS has been already extensively studied and verified its effectiveness. On the other hand, it is necessary to study more about the effectiveness of CPU consolidation. Key questions that must be answered are how effectively the CPU consolidation improves the energy efficiency and how to maximize the improvement. These questions are addressed in this paper. After understanding modern power management techniques and developing an appropriate power model, this paper provides an extensive set of hardware-based experimental results and makes suggestions about how to maximize energy efficiency improvement through CPU consolidation. In addition, this paper also presents new online CPU consolidation algorithms, which reduce the energy-delay product up to 13% compared with the Linux default DVFS algorithm.

NFRA: Generalized Network Flow-Based Resource Allocation for Hosting Centers

Abstract: Due to prohibitive cost of data center setup and maintenance, many small-scale businesses rely on hosting centers to provide the cloud infrastructure to run their workloads. Hosting centers host services of the clients on their behalf and guarantee quality of service as defined by service level agreements (SLAs.) To reduce energy consumption and to maximize profit it is critical to optimally allocate resources to meet client SLAs. Optimal allocation is a nontrivial task due to 1) resource heterogeneity where energy consumption of a client task varies depending on the allocated resources 2) lack of energy proportionality where energy cost for a task varies based on server utilization. In this paper, we introduce a generalized Network Flow-based Resource Allocation framework, called NFRA, for energy minimization and profit maximization. NFRA provides a unified framework to model profit maximization under a wide range of SLAs. We will demonstrate the simplicity of this unified framework by deriving optimal resource allocations for three different SLAs. We derive workload demands and server energy consumption data from SPECWeb2009 benchmark results to demonstrate the efficiency of NFRA framework.

System and method for reducing transitions on address buses

Abstract: An encoder and decoder provide coding of information communicated on buses. The encoder and decoder may use various combinations of techniques to reduce switching activity on an address bus.

Low-power data encoding/decoding for energy-efficient static random access memory design

Abstract: This study presents a new energy-efficient design for static random access memory (SRAM) using a low-power input data encoding and output data decoding stages. A data bit reordering algorithm is applied to the input data to increase the number of 0s that are going to be written into the SRAM array. Using SRAM cells which are more energy-efficient in writing a ‘0’ than a ‘1’ benefits from this, resulting in a reduction in the total power and energy consumptions of the whole memory. The input data encoding is performed using a simple circuit, which is built of multiplexers and inverters. After the read operation, data will be returned back to its initial form using a low-power data decoding circuit. Simulation results in an industrial and a predictive CMOS technology show that the proposed design for SRAM reduces the energy consumption of read and write operations considerably for some standard test images as input data to the memory. For instance, in writing pixels of Lenna test image into this SRAM and reading them back, 15 and 20% savings are observed for the energy consumption of write and read operations, respectively, compared with the normal write and read operations in standard SRAMs.

Dynamic voltage and frequency scaling for energy-efficient system design

Abstract: Demand for low power consumption in battery-powered computer systems has risen sharply. This is due to the fact that extending the service lifetime of these systems by reducing their power dissipation is a key customer requirement. Dynamic voltage and frequency scaling (DVFS) techniques have proven to be a highly effective in achieving low power consumption in various computer systems. The key idea behind DVFS technique is to adaptively scale the supply voltage level of the CPU so as to provide “just-enough” circuit speed to process the system workload while meeting total computation time and/or throughput constraints, and thereby, reduce the energy dissipation. This thesis presents intra-process DVFS techniques targeted toward both non real-time and real-time applications running on embedded system platforms. To enhance the amount of the CPU energy saving by DVFS, a technique called “workload decomposition” is proposed whereby the workload of a target program is decomposed in two parts: on-chip and off-chip. The on-chip workload signifies the CPU clock cycles that are required to execute instructions inside the CPU, whereas the off-chip workload captures the number of external memory access clock cycles that are required to perform external memory transactions. When combined with a DVFS technique to minimize the energy consumption, this workload decomposition method results in higher energy savings for memory-intensive applications. Notice that on-chip and off-chip workload cause different amount of power dissipation because on-chip workload requires the CPU to be executed while off-chip workload requires a proper system component such as memory. So, if the task workload is decomposed into on-chip and off-chip component, the system energy variation according to the CPU frequency can be predicted very accurately, which enables the development of a more effective DVFS approach for the system energy reduction. The proposed techniques have been implemented on two real computing systems: (1) the XScale-based embedded system platform built at USC and (2) the PXA255-processor based BitsyX system from ADS Inc. Energy savings with the proposed DVFS policies have been obtained by performing current measurements on real hardware.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.