64 research outputs found
Energy Saving Techniques for Phase Change Memory (PCM)
In recent years, the energy consumption of computing systems has increased
and a large fraction of this energy is consumed in main memory. Towards this,
researchers have proposed use of non-volatile memory, such as phase change
memory (PCM), which has low read latency and power; and nearly zero leakage
power. However, the write latency and power of PCM are very high and this,
along with limited write endurance of PCM present significant challenges in
enabling wide-spread adoption of PCM. To address this, several
architecture-level techniques have been proposed. In this report, we review
several techniques to manage power consumption of PCM. We also classify these
techniques based on their characteristics to provide insights into them. The
aim of this work is encourage researchers to propose even better techniques for
improving energy efficiency of PCM based main memory.Comment: Survey, phase change RAM (PCRAM
Deep supervised learning using local errors
Error backpropagation is a highly effective mechanism for learning
high-quality hierarchical features in deep networks. Updating the features or
weights in one layer, however, requires waiting for the propagation of error
signals from higher layers. Learning using delayed and non-local errors makes
it hard to reconcile backpropagation with the learning mechanisms observed in
biological neural networks as it requires the neurons to maintain a memory of
the input long enough until the higher-layer errors arrive. In this paper, we
propose an alternative learning mechanism where errors are generated locally in
each layer using fixed, random auxiliary classifiers. Lower layers could thus
be trained independently of higher layers and training could either proceed
layer by layer, or simultaneously in all layers using local error information.
We address biological plausibility concerns such as weight symmetry
requirements and show that the proposed learning mechanism based on fixed,
broad, and random tuning of each neuron to the classification categories
outperforms the biologically-motivated feedback alignment learning technique on
the MNIST, CIFAR10, and SVHN datasets, approaching the performance of standard
backpropagation. Our approach highlights a potential biological mechanism for
the supervised, or task-dependent, learning of feature hierarchies. In
addition, we show that it is well suited for learning deep networks in custom
hardware where it can drastically reduce memory traffic and data communication
overheads
Алгоритмы, программная архитектура и информационная технология моделирования методов масштабирования скоростей параллельных процессоров вычислительного кластера
Предложены алгоритмы масштабирования скоростей параллельных процессоров многопроцессорных систем и вычислительных кластеров, имеющих гомогенную и гетерогенную архитектуру. Разработаны программная архитектура и информационная технология для моделирования работы алгоритмов, использующих различные методы распределения заданий на процессоры, приведены примеры и результаты их экспериментального исследования, подтверждающие их эффективность для построения энергоэффективных расписаний выполнения заданий с директивными сроками выполнения в многопроцессорных системах и вычислительных кластерах.There have been proposed the algorithms for speed scaling on parallel processors for multiprocessor systems and computing clusters with homogeneous and heterogeneous architectures. Software architecture and information technology for simulation of algorithms using a variety of methods for allocating tasks to processors are developed. There are examples and the results of their experimental studies proving its effectiveness for constructing energy efficient scheduling task execution with deadlines in multiprocessor systems and computing clusters
Leveraging heterogeneity for energy minimization in data centers
Energy consumption in data centers is nowadays a critical objective because of its dramatic environmental and economic impact. Over the last years, several approaches have been proposed to tackle the energy/cost optimization problem, but most of them have failed on providing an analytical model to target both the static and dynamic optimization domains for complex heterogeneous data centers. This paper proposes and solves an optimization problem for the energy-driven configuration of a heterogeneous data center. It also advances in the proposition of a new mechanism for task allocation and distribution of workload. The combination of both approaches outperforms previous published results in the field of energy minimization in heterogeneous data centers and scopes a promising area of research
Cost Function based Event Triggered Model Predictive Controllers - Application to Big Data Cloud Services
International audienceHigh rate cluster reconfigurations is a costly issue in Big Data Cloud services. Current control solutions manage to scale the cluster according to the workload, however they do not try to minimize the number of system reconfigurations. Event-based control is known to reduce the number of control updates typically by waiting for the system states to degrade below a given threshold before reacting. However, computer science systems often have exogenous inputs (such as clients connections) with delayed impacts that can enable to anticipate states degradation. In this paper, a novel event-triggered approach is proposed. This triggering mechanism relies on a Model Predictive Controller and is defined upon the value of the optimal cost function instead of the state or output error. This controller reduces the number of control changes, in the normal operation mode, through constraints in the MPC formulation but also assures a very reactive behavior to changes of exogenous inputs. This novel control approach is evaluated using a model validated on a real Big Data system. The controller efficiently scales the cluster according to specifications, meanwhile reducing its reconfigurations
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