Microarchitectural techniques to reduce energy consumption in the memory hierarchy

This thesis states that dynamic profiling of the memory reference stream can improve energy and performance in the memory hierarchy. The research presented in this theses provides multiple instances of using lightweight hardware structures to profile the memory reference stream. The objective of this research is to develop microarchitectural techniques to reduce energy consumption at different levels of the memory hierarchy. Several simple and implementable techniques were developed as a part of this research. One of the techniques identifies and eliminates redundant refresh operations in DRAM and reduces DRAM refresh power. Another, reduces leakage energy in L2 and higher level caches for multiprocessor systems. The emphasis of this research has been to develop several techniques of obtaining energy savings in caches using a simple hardware structure called the counting Bloom filter (CBF). CBFs have been used to predict L2 cache misses and obtain energy savings by not accessing the L2 cache on a predicted miss. A simple extension of this technique allows CBFs to do way-estimation of set associative caches to reduce energy in cache lookups. Another technique using CBFs track addresses in a Virtual Cache and reduce false synonym lookups. Finally this thesis presents a technique to reduce dynamic power consumption in level one caches using significance compression. The significant energy and performance improvements demonstrated by the techniques presented in this thesis suggest that this work will be of great value for designing memory hierarchies of future computing platforms.Ph.D.Committee Chair: Lee, Hsien-Hsin S.; Committee Member: Cahtterjee,Abhijit; Committee Member: Mukhopadhyay, Saibal; Committee Member: Pande, Santosh; Committee Member: Yalamanchili, Sudhaka

Prediction of Wave Energy Potential in India: A Fuzzy-ANN Approach

The conversion efficiency of wave energy converters is not only unsatisfactory but also expensive, which is why the popularity of wave energy as an alternative to conventional energy sources is subjacent. This means that besides wave height and period, there are many other factors which influence the amount of “utilizable” wave energy potential. The present study attempts to identify these important factors and predict power potential as a function of these factors. Accordingly, a polynomial neural network was utilized, and fuzzy logic was applied to identify the most important factors. According to the results, wave height was found to have the maximum importance followed by wave period, water depth, and salinity. In total, 12 different neural network models were developed to predict the same output, among which the model with all of the 4 inputs was found to have optimal performance

Hospital-based surveillance of enteric parasites in Kolkata

<p>Abstract</p> <p>Background</p> <p>Diarrhoea is the second leading cause of illness and death in developing countries and the second commonest cause of death due to infectious diseases among children under five in such countries. Parasites, as well as bacterial and viral pathogens, are important causes of diarrhoea. However, parasitic infections are sometimes overlooked, leading after a period of time to an uncertain aetiology. In this paper we report the prevalence of <it>Giardia lamblia</it>, <it>Entamoeba histolytica </it>and <it>Cryptosporidium </it>sp. in and around Kolkata.</p> <p>Findings</p> <p>A hospital-based laboratory surveillance study was conducted among the patients admitted between November 2007 and October 2008 to the Infectious Diseases (ID) Hospital (Population = 1103) with diarrhoeal complaints. Of the 1103 samples collected, 147 were positive for <it>Giardia lamblia</it>, 84 for <it>Cryptosporidium </it>sp. and 51 for <it>Entamoeba histolytica</it>. For all these parasites there was a high rate of mixed infection with common enteric viruses and bacteria such as Rotavirus, <it>Vibrio cholerae </it>and <it>Shigella </it>sp. There were also cases of co-infection with all other diarrheogenic pathogens. The age group ≥ 5 years had the highest prevalence of parasites whereas the age group >5 – 10 years was predominantly infected with <it>Giardia lamblia </it>(p =< 0.001; Odds ratio (OR) = 3.937; 95% Confidence interval (CI) = 1.862 – 8.326) and with all parasites (p = 0.040; OR = 2.043; 95% CI = 1.033 – 4.039). The age group >10 – 20 years could also be considered at risk for <it>G. lamblia </it>(p = 0.009; OR = 2.231; 95% CI = 1.223 – 4.067). Month-wise occurrence data showed an endemic presence of <it>G. lamblia </it>whereas <it>Cryptosporidium </it>sp. and <it>E. histolytica </it>occurred sporadically. The GIS study revealed that parasites were more prevalent in areas such as Tangra, Tiljala and Rajarhat, which are mainly slum areas. Because most of the population surveyed was in the lower income group, consumption of contaminated water and food could be the major underlying cause of parasitic infestations.</p> <p>Conclusion</p> <p>This study provides important information on the occurrence and distribution of three important intestinal parasites and indicates their diarrheogenic capacity in Kolkata and surrounding areas.</p

System-Level Characterization of Datacenter Applications

In recent years, a number of benchmark suites have been created for the “Big Data ” domain, and a number of such applications fit the client-server paradigm. A large volume of recent literature in characterizing “Big Data ” applications have largely focused on two extremes of the characterization spectrum. On one hand, multiple studies have focused on client-side performance. These involve fine-tuning server-side parameters for an application to get the best client-side performance. On the other extreme, characterization fo-cuses on picking one set of client-side parameters and then reporting the server microarchitectural statistics under those assumptions. While the two ends of the spectrum present in-teresting results, this paper argues that they are not enough, and in some cases, undesirable, to drive system-wide archi-tectural decisions in datacenter design. This paper shows that for the purposes of designing an efficient datacenter, detailed microarchitectural characteri-zation of “Big Data ” applications is an overkill. It identi-fies four main system-level macro-architectural features and shows that these features are more representative of an ap-plication’s system level behavior. To this end, a number of datacenter applications from a variety of benchmark suites are evaluated and classified into these previously identified macro-architectural features. Based on this analysis, the paper further shows that each application class will benefit from a very different server configuration leading to a highly efficient, cost-effective datacenter

MTrainS: Improving DLRM training efficiency using heterogeneous memories

Recommendation models are very large, requiring terabytes (TB) of memory during training. In pursuit of better quality, the model size and complexity grow over time, which requires additional training data to avoid overfitting. This model growth demands a large number of resources in data centers. Hence, training efficiency is becoming considerably more important to keep the data center power demand manageable. In Deep Learning Recommendation Models (DLRM), sparse features capturing categorical inputs through embedding tables are the major contributors to model size and require high memory bandwidth. In this paper, we study the bandwidth requirement and locality of embedding tables in real-world deployed models. We observe that the bandwidth requirement is not uniform across different tables and that embedding tables show high temporal locality. We then design MTrainS, which leverages heterogeneous memory, including byte and block addressable Storage Class Memory for DLRM hierarchically. MTrainS allows for higher memory capacity per node and increases training efficiency by lowering the need to scale out to multiple hosts in memory capacity bound use cases. By optimizing the platform memory hierarchy, we reduce the number of nodes for training by 4-8X, saving power and cost of training while meeting our target training performance

Rapid immunochromatographic test: An evolving tool for diagnosis of scrub typhus

Background: Scrub typhus is prevalent in many districts of South Bengal throughout the year where an average temperature of 20–35°C, which contributes to the spread of Leptotrombidium deliense. However, its diagnosis remains complicated by the lack of readily available and validated assays, the non-specificity of clinical symptoms on admission, and even non-availability of the pathognomonic eschar in most of the cases. Aims and Objectives: This study was carried out to evaluate the rapid immunochromatographic test (RICT) for early detection of scrub typhus for using it as an early diagnostic tool at the field level. Materials and Methods: This cross-sectional study in which 181 serum samples from clinically suspected cases (after excluding dengue, malaria, Japanese encephalitis, and typhoid fever) collected over 13 months were processed for the detection of immunoglobulin M (IgM) antibodies for scrub typhus by enzyme-linked immunosorbent assay (ELISA) and rapid test. Results: Considering IgM ELISA for scrub typhus as the gold standard, the sensitivity, specificity, positive predictive value, and negative predictive value for RICT were found to be 100%, 86.87%, 50%, and 100%, respectively. Conclusion: RICT is a simple, rapid, and reliable assay for diagnosis of scrub typhus, capable of providing accurate results quickly and is highly suitable for field deployment in remote areas with limited medical support

Emerging trends in the etiology of enteric pathogens as evidenced from an active surveillance of hospitalized diarrhoeal patients in Kolkata, India

Background: This study was conducted to determine the etiology of diarrhoea in a hospital setting in Kolkata. Active surveillance was conducted for 2 years on two random days per week by enrolling every fifth diarrhoeal patient admitted to the Infectious Diseases and Beliaghata General Hospital in Kolkata. Results: Most of the patients (76.1%) had acute watery diarrhoea in association with vomiting (77.7%) and some dehydration (92%). Vibrio cholerae O1, Rotavirus and Giardia lamblia were the important causes of diarrhoea. Among Shigella spp, S. flexneri 2a and 3a serotypes were most predominantly isolated. Enteric viruses, EPEC and EAEC were common in children <5 year age group. Atypical EPEC was comparatively higher than the typical EPEC. Multidrug resistance was common among V. cholerae O1 and Shigella spp including tetracycline and ciprofloxacin. Polymicrobial infections were common in all age groups and 27.9% of the diarrhoea patients had no potential pathogen. Conclusions: Increase in V. cholerae O1 infection among <2 years age group, resistance of V. cholerae O1 to tetracycline, rise of untypable S. flexnerii, higher proportion of atypical EPEC and G. lamblia and polymicrobial etiology are some of the emerging trends observed in this diarrhoeal disease surveillance

Performance analysis of NVMe SSDs and their implication on real world databases

The storage subsystem has undergone tremendous innova-tion in order to keep up with the ever-increasing demand for throughput. Non Volatile Memory Express (NVMe) based solid state devices are the latest development in this do-main, delivering unprecedented performance in terms of la-tency and peak bandwidth. NVMe drives are expected to be particularly beneficial for I/O intensive applications, with databases being one of the prominent use-cases. This paper provides the first, in-depth performance analy-sis of NVMe drives. Combining driver instrumentation with system monitoring tools, we present a breakdown of access times for I/O requests throughout the entire system. Fur-thermore, we present a detailed, quantitative analysis of all the factors contributing to the low-latency, high-throughput characteristics of NVMe drives, including the system soft-ware stack. Lastly, we characterize the performance of mul-tiple cloud databases (both relational and NoSQL) on state-of-the-art NVMe drives, and compare that to their perfor-mance on enterprise-class SATA-based SSDs. We show that NVMe-backed database applications deliver up to 8 × su-perior client-side performance over enterprise-class, SATA-based SSDs