ABSTRACT
We study and characterize the performance of operations in an important class of applications on GPUs and Many Integrated Core (MIC) architectures. Our work is motivated by applications that analyze low-dimensional spatial datasets captured by high resolution sensors, such as image datasets obtained from whole slide tissue specimens using microscopy scanners. Common operations in these applications involve the detection and extraction of objects (object segmentation), the computation of features of each extracted object (feature computation), and characterization of objects based on these features (object classification). In this work, we have identify the data access and computation patterns of operations in the object segmentation and feature computation categories. We systematically implement and evaluate the performance of these operations on modern CPUs, GPUs, and MIC systems for a microscopy image analysis application. Our results show that the performance on a MIC of operations that perform regular data access is comparable or sometimes better than that on a GPU. On the other hand, GPUs are significantly more efficient than MICs for operations that access data irregularly. This is a result of the low performance of MICs when it comes to random data access. We also have examined the coordinated use of MICs and CPUs. Our experiments show that using a performance aware task strategy for scheduling application operations improves performance about 1.29× over a first-come-first-served strategy. This allows applications to obtain high performance efficiency on CPU-MIC systems - the example application attained an efficiency of 84% on 192 nodes (3072 CPU cores and 192 MICs).
ABSTRACT
AIM: This study aimed to investigate the erosive potential of these drinks using human enamel in vitro. MATERIAL AND METHODS: A range of bottled, still flavoured water drinks available in the UK were investigated and their erosive potential was compared by measuring pH and titratable acidity. Six beverages were chosen for the main study and also both a negative (distilled water) and positive control (orange juice). Human enamel specimens were prepared, sectioned and varnished leaving an exposure window visible to have contact with test solutions. Each specimen was randomly allocated in groups of six. Each group was exposed to 20 ml of one of the eight test solutions for 10, 30, 60 and 90 min. Quantitative light-induced fluorescence was used to ensure the teeth selected were free of artefacts and diseased areas. Erosion was measured using non-contact optical profilometry. RESULTS: Enamel loss occurred with all test drinks and the positive control (p<0.05) and in most cases the still water drinks were just as erosive as the positive control. Only vitamin water at 30 min was not significantly different from the negative control (p= 0.86), All drinks tested resulted in significant enamel loss (p<0.01). These results may indicate that consumers should think of still water beverages as potentially acidic drinks rather than just flavoured healthy water alternatives. CONCLUSION: This study indicates the need for preventive advice to be given by dentists about such beverages and therefore ultimately to make patients and consumers more aware of hidden erosive risks.
Subject(s)
Humans , Male , Female , Tooth Erosion , Beverages , Dental EnamelABSTRACT
We introduce a region template abstraction and framework for the efficient storage, management and processing of common data types in analysis of large datasets of high resolution images on clusters of hybrid computing nodes. The region template abstraction provides a generic container template for common data structures, such as points, arrays, regions, and object sets, within a spatial and temporal bounding box. It allows for different data management strategies and I/O implementations, while providing a homogeneous, unified interface to applications for data storage and retrieval. A region template application is represented as a hierarchical dataflow in which each computing stage may be represented as another dataflow of finer-grain tasks. The execution of the application is coordinated by a runtime system that implements optimizations for hybrid machines, including performance-aware scheduling for maximizing the utilization of computing devices and techniques to reduce the impact of data transfers between CPUs and GPUs. An experimental evaluation on a state-of-the-art hybrid cluster using a microscopy imaging application shows that the abstraction adds negligible overhead (about 3%) and achieves good scalability and high data transfer rates. Optimizations in a high speed disk based storage implementation of the abstraction to support asynchronous data transfers and computation result in an application performance gain of about 1.13×. Finally, a processing rate of 11,730 4K×4K tiles per minute was achieved for the microscopy imaging application on a cluster with 100 nodes (300 GPUs and 1,200 CPU cores). This computation rate enables studies with very large datasets.