Saturn: An Optimized Data System for Large Model Deep Learning Workloads

Large language models such as GPT-3 & ChatGPT have transformed deep learning (DL), powering applications that have captured the public's imagination. These models are rapidly being adopted across domains for analytics on various modalities, often by finetuning pre-trained base models. Such models need multiple GPUs due to both their size and computational load, driving the development of a bevy of "model parallelism" techniques & tools. Navigating such parallelism choices, however, is a new burden for end users of DL such as data scientists, domain scientists, etc. who may lack the necessary systems knowhow. The need for model selection, which leads to many models to train due to hyper-parameter tuning or layer-wise finetuning, compounds the situation with two more burdens: resource apportioning and scheduling. In this work, we tackle these three burdens for DL users in a unified manner by formalizing them as a joint problem that we call SPASE: Select a Parallelism, Allocate resources, and SchedulE. We propose a new information system architecture to tackle the SPASE problem holistically, representing a key step toward enabling wider adoption of large DL models. We devise an extensible template for existing parallelism schemes and combine it with an automated empirical profiler for runtime estimation. We then formulate SPASE as an MILP. We find that direct use of an MILP-solver is significantly more effective than several baseline heuristics. We optimize the system runtime further with an introspective scheduling approach. We implement all these techniques into a new data system we call Saturn. Experiments with benchmark DL workloads show that Saturn achieves 39-49% lower model selection runtimes than typical current DL practice.Comment: Under submission at VLDB. Code available: https://github.com/knagrecha/saturn. 12 pages + 3 pages references + 2 pages appendi

Application of cepstrum analysis and linear predictive coding for motor imaginary task classification

In this paper, classification of electroencephalography (EEG) signals of motor imaginary tasks is studied using cepstrum analysis and linear predictive coding (LPC). The Brain-Computer Interface (BCI) competition III dataset IVa containing motor imaginary tasks for right hand and foot of five subjects are used. The data was preprocessed by applying whitening and then filtering the signal followed by feature extraction. A random forest classifier is then trained using the cepstrum and LPC features to classify the motor imaginary tasks. The resulting classification accuracy is found to be over 90%. This research shows that concatenating appropriate different types of features such as cepstrum and LPC features hold some promise for the classification of motor imaginary tasks, which can be helpful in the BCI context

Design of Low Power MAX Operator for Multi-valued Logic System

AbstractA voltage-mode three transistor based MAX circuit for implementation of multi-valued logic (MVL) system is proposed in this paper. The proposed MAX operates at very low power consumption ranging in micro watts. To evaluate MAX performance, a NOR gate realization is done and compared to standard CMOS NOR gate. The HSpice simulation result confirms the MAX based NOR gate to operate with minimal delay at low power level. The simulations have been performed on 180nm technology

Role of yoga in improving pulmonary efficiency in post-menopausal women

Background: Menopause is a natural transition in a women’s life. Menopausal transition has been linked to impairment of respiratory function. Female hormones play an important role in overall lung health. Yoga is an ancient Indian science as well as the way of life, which includes practice of yogasana in specific posture and pranayama which includes the regulated breathing techniques. The aim of the study was to evaluate the cumulative effect of practicing yoga and pranayamas on certain respiratory parameters and physical characteristics in post-menopausal women. Methods: A total of forty post-menopausal women (46-60 years) were divided into two groups. Group I was control group (women not doing yoga) and group II was regularly doing yoga for one year. Based on the duration of yoga- pranayama and meditation, the respiratory parameters such as VC, FVC, FEV1, PEFR, and FEF50 was measured with the help of vitalograph (pneumotrac; 11). Data collected were compiled, categorized and statistically analyzed, t-test was used for comparing the effect of yoga between the two groups and p≤0.05 was considered as statistically significant. Results: One-year yoga showed a significant decrement in Body weight (p<0.001) and Body Mass Index (p<0.01). Group II showed significant improvement (p<0.001) in all the respiratory parameters such as VC, FVC, FEV1, PEFR, and FEF50 when compared to respective control group. Respiratory rate was decreased significantly (p<0.0001) and Breath hold time was increased significantly (p<0.0001) when compared to control group.   Conclusions: The present study demonstrated that the one-year of yogic practice is suitable for improving pulmonary efficiency and physical characteristics in post-menopausal women

Awareness and knowledge about bioterrorism among medical students at a University in Malaysia

The use of biological agents as weapons in warfare has been practiced since antiquity and is on the rise recently. In the event of an act of bioterrorism, health care professionals have to be prepared to identify and counter such incidents. They have to recognize and initiate rapid response to acts of bioterrorism underlining the importance of awareness and preparedness for bioterrorism. A closed questionnaire based survey, conducted among medical students, to assess their awareness and knowledge on bioterrorism included questions on biosafety, biosecurity, target population, biological agents, role of doctors and hospitals, response and scope. Results were analyzed by using simple statistical applications. Of 132 respondents, 64% were aware of the term bioterrorism. Students were aware of important etiological agents used in bioterrorism; Bacillus anthracis (52%), Ebola virus (58%), Small pox (51%), Vibrio cholerae (41%) and Clostridium botulinum (28%). Majority of students were aware about the importance of identifying illness, and reporting to concerned health officials and 95% respondents opined that, inclusion of bioterrorism in curriculum provides scope for expansion of preparedness. This study indicates a need to include bioterrorism as a topic in curriculum, thereby providing basic knowledge and preparedness to respond to bioterrorism

A method for temporal fault tree analysis using intuitionistic fuzzy set and expert elicitation

YesTemporal fault trees (TFTs), an extension of classical Boolean fault trees, can model time-dependent failure behaviour of dynamic systems. The methodologies used for quantitative analysis of TFTs include algebraic solutions, Petri nets (PN), and Bayesian networks (BN). In these approaches, precise failure data of components are usually used to calculate the probability of the top event of a TFT. However, it can be problematic to obtain these precise data due to the imprecise and incomplete information about the components of a system. In this paper, we propose a framework that combines intuitionistic fuzzy set theory and expert elicitation to enable quantitative analysis of TFTs of dynamic systems with uncertain data. Experts’ opinions are taken into account to compute the failure probability of the basic events of the TFT as intuitionistic fuzzy numbers. Subsequently, for the algebraic approach, the intuitionistic fuzzy operators for the logic gates of TFT are defined to quantify the TFT. On the other hand, for the quantification of TFTs via PN and BN-based approaches, the intuitionistic fuzzy numbers are defuzzified to be used in these approaches. As a result, the framework can be used with all the currently available TFT analysis approaches. The effectiveness of the proposed framework is illustrated via application to a practical system and through a comparison of the results of each approach.This work was supported in part by the Mobile IOT: Location Aware project (grant no. MMUE/180025) and Indoor Internet of Things (IOT) Tracking Algorithm Development based on Radio Signal Characterisation project (grant no. FRGS/1/2018/TK08/MMU/02/1). This research also received partial support from DEIS H2020 project (grant no. 732242)

Rebar corrosion due to chlorides in synergy with sodium, potassium, and magnesium

The ability of steel reinforced concrete to withstand long service life is ensured by the strong binding between the concrete and the rebar. Although rebar corrosion deterioration in the presence of chlorides is well known, it is important to note that these anions are never present in isolation, i.e., other cations are also present within the exposed environment. Consequently, a study was conducted to investigate the rebar deterioration due to chlorides in the presence of different cations. A well-controlled laboratory experiment for assessing the corrosivity of sodium chloride, potassium chloride and magnesium chloride was conducted. The galvanostatic pulse technique was used to investigate the concrete-steel interfacial structure, which was modelled after a modified Randles circuit. Analysis revealed influences of the associated cations during the rebar corrosion process. A normalisation approach was used to compare chloride attacks on the rebar due to different salt solutions. Results suggest that chloride attacks in the presence of sodium cations are relatively corrosive

3D printed biomodels for flow visualization in stenotic vessels: an experimental and numerical study

Atherosclerosis is one of the most serious and common forms of cardiovascular disease and a major cause of death and disability worldwide. It is a multifactorial and complex disease that promoted several hemodynamic studies. Although in vivo studies more accurately represent the physiological conditions, in vitro experiments more reliably control several physiological variables and most adequately validate numerical flow studies. Here, a hemodynamic study in idealized stenotic and healthy coronary arteries is presented by applying both numerical and in vitro approaches through computational fluid dynamics simulations and a high-speed video microscopy technique, respectively. By means of stereolithography 3D printing technology, biomodels with three different resolutions were used to perform experimental flow studies. The results showed that the biomodel printed with a resolution of 50 μm was able to most accurately visualize flow due to its lowest roughness values (Ra = 1.8 μm). The flow experimental results showed a qualitatively good agreement with the blood flow numerical data, providing a clear observation of recirculation regions when the diameter reduction reached 60%.This work was supported by FCT-Fundacao para a Ciencia e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020, UIDB/04077/2020, and NORTE-01-0145-FEDER-030171, funded by COMPETE2020, NORTE 2020, PORTUGAL 2020, and FEDER. This project received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 798014. This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 828835