Dwelling environments in Taichung, Taiwan : an analysis for the development of urban settlements.

Thesis. 1979. M.Arch.A.S.--Massachusetts Institute of Technology. Dept. of Architecture.MICROFICHE COPY AVAILABLE IN ARCHIVES AND ROTCH.Bibliography: p. 52.M.Arch.A.S

Accelerating Data Loading in Deep Neural Network Training

Data loading can dominate deep neural network training time on large-scale systems. We present a comprehensive study on accelerating data loading performance in large-scale distributed training. We first identify performance and scalability issues in current data loading implementations. We then propose optimizations that utilize CPU resources to the data loader design. We use an analytical model to characterize the impact of data loading on the overall training time and establish the performance trend as we scale up distributed training. Our model suggests that I/O rate limits the scalability of distributed training, which inspires us to design a locality-aware data loading method. By utilizing software caches, our method can drastically reduce the data loading communication volume in comparison with the original data loading implementation. Finally, we evaluate the proposed optimizations with various experiments. We achieved more than 30x speedup in data loading using 256 nodes with 1,024 learners.Comment: 11 pages, 12 figures, accepted for publication in IEEE International Conference on High Performance Computing, Data and Analytics (HiPC) 201

Hierarchically Tiled Arrays as high-level programming abstractions for dataflow runtime systems

In the foreseeable future, high-performance supercomputers will continue to evolve in the direction of attempting to build distributed, immensely parallel and highly heterogeneous machines. It is well known that in order to utilize these machines, good parallel programs are essential. However, conventional parallel programming models were created when supercomputers were smaller and more homogeneous. It is not clear whether these models will enable the same level of productivity for the next generation supercomputers. It is expected that intermediate runtime systems between software applications and the underlying hardware machine architecture will help abstract away the extreme complexity of future large-scale machines. In the recent past, there have been growing interests in dataflow execution models due to their flexibility in making dynamic decisions. Despite their advantages, the dataflow runtime systems tend to have a low-level programming interface that is difficult to tame. It requires the programmer to decompose the computation and write program to construct dependence graph explicitly, resulting in programs that are difficult to build, debug and maintain. In this thesis, we repurpose the Hierarchically Tiled Array (HTA) programming model for improving the programmability of the dataflow runtime systems. HTA facilitates parallel programming by letting the programmer express algorithms as tiled array operations which contains implicit parallelism. We propose a design to map an HTA program to a dataflow task dependence graph dynamically, so that the programmer can write conventional HTA programs while enjoying the benefits provided by the underlying dataflow runtime system. As a proof of concepts, we implemented our design for the shared memory environment and implemented a variety of benchmarks for performance evaluation. We found that, for applications with high asynchrony and sparse data dependences, our implementation results in simpler programs than those obtained by using the dataflow runtime programming interface and delivers superior performance results than OpenMP using parallel for loops. We also learned about the scalability issues in our current design and propose solutions as possible future work

Web dynamics : modeling, simulation, and experimentation

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 1996.Includes bibliographical references (leaf 73).by Clara C. Yang.M.S

(PS)(2): protein structure prediction server

Protein structure prediction provides valuable insights into function, and comparative modeling is one of the most reliable methods to predict 3D structures directly from amino acid sequences. However, critical problems arise during the selection of the correct templates and the alignment of query sequences therewith. We have developed an automatic protein structure prediction server, (PS)(2), which uses an effective consensus strategy both in template selection, which combines PSI-BLAST and IMPALA, and target–template alignment integrating PSI-BLAST, IMPALA and T-Coffee. (PS)(2) was evaluated for 47 comparative modeling targets in CASP6 (Critical Assessment of Techniques for Protein Structure Prediction). For the benchmark dataset, the predictive performance of (PS)(2), based on the mean GTD_TS score, was superior to 10 other automatic servers. Our method is based solely on the consensus sequence and thus is considerably faster than other methods that rely on the additional structural consensus of templates. Our results show that (PS)(2), coupled with suitable consensus strategies and a new similarity score, can significantly improve structure prediction. Our approach should be useful in structure prediction and modeling. The (PS)(2) is available through the website at

The Behavior of Retaining Walls Under 1999 Chi-Chi Earthquake

This paper reports failure of retaining structures related to the 1999 Chi-Chi earthquake. On September 21 of 1999, an earthquake with a magnitude of 7.3 on the Richter scale struck Taiwan. At the site near Tou-Sheh, overturning failure was observed on a 2.5 m-high gravity wall located near the epicenter. At the site near the Temple of Ten-thousand Buddha, a masonry wall constructed with cobble was damaged. Upper part of wall shifted outward about 0.2 m with respect to its lower part. At the site of Cinema-Culture Town, a gravity wall built on top of the Che-Lung-Pu fault was severely damaged. The heel of the wall was uplifted by the fault rupture. At the parking lot of the Lalu Resort, the retaining wall constructed on a slope moved down the slope during the earthquake. A circular sliding surface can be observed in the collapsed backfill. At the entrance of National Chi-Nan University, a geogrid-reinforced wall was severely damaged during the earthquake. Reinforcing strips were pulled out under the seismic load and fill materials fell out

Application and comparison of scoring indices to predict outcomes in patients with healthcare-associated pneumonia

Introduction: Healthcare-associated pneumonia HCAP is a relatively new category of pneumonia. It refers to infections that occur prior to hospital admission in patients with specific risk factors following contact or exposure to a healthcare environment. There is currently no scoring index to predict the outcomes of HCAP patients. We applied and compared different community acquired pneumonia CAP scoring indices to predict 30-day mortality and 3-day and 14-day intensive care unit ICU admission in patients with HCAP. Methods: We conducted a retrospective cohort study based on an inpatient database from six medical centers, recruiting a total of 444 patients with HCAP between 1 January 2007 and 31 December 2007. Pneumonia severity scoring indices including PSI pneumonia severity index, CURB 65 confusion, urea, respiratory rate, blood pressure , age 65, IDSA/ATS Infectious Diseases Society of America/American Thoracic Society, modified ATS rule, SCAP severe community acquired pneumonia, SMART-COP systolic blood pressure, multilobar involvement, albumin, respiratory rate, tachycardia, confusion, oxygenation, pH, SMRT- CO systolic blood pressure, multilobar involvement, respiratory rate, tachycardia, confusion, oxygenation, and SOAR systolic blood pressure, oxygenation, age, respiratory rate were calculated for each patient. Patient characteristics, co-morbidities, pneumonia pathogen culture results, length of hospital stay LOS, and length of ICU stay were also recorded. Results: PSI > 90 has the highest sensitivity in predicting mortality, followed by CURB-65 >= 2 and SCAP > 9 SCAP score area under the curve AUC: 0.71, PSI AUC: 0.70 and CURB-65 AUC: 0.66. Compared to PSI, modified ATS, IDSA/ATS, SCAP, and SMART-COP were easy to calculate. For predicting ICU admission Day 3 and Day 14, modified ATS AUC: 0.84, 0.82 , SMART-COP AUC: 0.84, 0.82, SCAP AUC: 0.82, 0.80 and IDSA/ ATS AUC: 0.80, 0 .79 performed better statistically significant difference than PSI, CURB- 65, SOAR and SMRT-CO. Conclusions: The utility of the scoring indices for risk assessment in patients with healthcare-associated pneumonia shows that the scoring indices originally designed for CAP can be applied to HCAP