Effects of Different Parameter Settings for 3D Data Smoothing and Mesh Simplification on Near Real-Time 3D Reconstruction of High Resolution Bioceramic Bone Void Filling Medical Images

Three-dimensional reconstruction plays a vital role in assisting doctors and surgeons in diagnosing the healing progress of bone defects. Common three-dimensional reconstruction methods include surface and volume rendering. As the focus is on the shape of the bone, this study omits the volume rendering methods. Many improvements have been made to surface rendering methods like Marching Cubes and Marching Tetrahedra, but not many on working towards real-time or near real-time surface rendering for large medical images and studying the effects of different parameter settings for the improvements. Hence, this study attempts near real-time surface rendering for large medical images. Different parameter values are experimented on to study their effect on reconstruction accuracy, reconstruction and rendering time, and the number of vertices and faces. The proposed improvement involving three-dimensional data smoothing with convolution kernel Gaussian size 5 and mesh simplification reduction factor of 0.1 is the best parameter value combination for achieving a good balance between high reconstruction accuracy, low total execution time, and a low number of vertices and faces. It has successfully increased reconstruction accuracy by 0.0235%, decreased the total execution time by 69.81%, and decreased the number of vertices and faces by 86.57% and 86.61%, respectively.

SAT-Hadoop-Processor: A Distributed Remote Sensing Big Data Processing Software for Earth Observation Applications

Nowadays, several environmental applications take advantage of remote sensing techniques. A considerable volume of this remote sensing data occurs in near real-time. Such data are diverse and are provided with high velocity and variety, their pre-processing requires large computing capacities, and a fast execution time is critical. This paper proposes a new distributed software for remote sensing data pre-processing and ingestion using cloud computing technology, specifically OpenStack. The developed software discarded 86% of the unneeded daily files and removed around 20% of the erroneous and inaccurate datasets. The parallel processing optimized the total execution time by 90%. Finally, the software efficiently processed and integrated data into the Hadoop storage system, notably the HDFS, HBase, and Hive.

Effects of Different Parameter Settings for 3D Data Smoothing and Mesh Simplification on Towards Near Real-Time 3D Reconstruction of High Resolution Bioceramic Bone Void Filling Medical Images

Three-dimensional reconstruction plays an important role in assisting doctors and surgeons in diagnosing bone defects’ healing progress. Common three-dimensional reconstruction methods include surface and volume rendering. As the focus is on the shape of the bone, volume rendering is omitted. Many improvements have been made on surface rendering methods like Marching Cubes and Marching Tetrahedra, but not many on working towards real-time or near real-time surface rendering for large medical images, and studying the effects of different parameter settings for the improvements. Hence, in this study, an attempt towards near real-time surface rendering for large medical images is made. Different parameter values are experimented on to study their effect on reconstruction accuracy, reconstruction and rendering time, and the number of vertices and faces. The proposed improvement involving three-dimensional data smoothing with convolution kernel Gaussian size 0.5 and mesh simplification reduction factor of 0.1, is the best parameter value combination for achieving a good balance between high reconstruction accuracy, low total execution time, and a low number of vertices and faces. It has successfully increased the reconstruction accuracy by 0.0235%, decreased the total execution time by 69.81%, and decreased the number of vertices and faces by 86.57% and 86.61% respectively.

Effective Utilization of Resources Through Optimal Allocation and Opportunistic Migration of Virtual Machines in Cloud Environment

Cloud computing has become more prominent, and it is used in large data centers. Distribution of well-organized resources (bandwidth, CPU, and memory) is the major problem in the data centers. The genetically enhanced shuffling frog leaping algorithm (GESFLA) framework is proposed to select the optimal virtual machines to schedule the tasks and allocate them in physical machines (PMs). The proposed GESFLA-based resource allocation technique is useful in minimizing the wastage of resource usage and also minimizes the power consumption of the data center. The proposed GESFL algorithm is compared with task-based particle swarm optimization (TBPSO) for efficiency. The experimental results show the excellence of GESFLA over TBPSO in terms of resource usage ratio, migration time, and total execution time. The proposed GESFLA framework reduces the energy consumption of data center up to 79%, migration time by 67%, and CPU utilization is improved by 9% for Planet Lab workload traces. For the random workload, the execution time is minimized by 71%, transfer time is reduced up to 99%, and the CPU consumption is improved by 17% when compared to TBPSO.

Negative Cost Girth Problem Using Map-Reduce Framework

On a graph with a negative cost cycle, the shortest path is undefined, but the number of edges of the shortest negative cost cycle could be computed. It is called Negative Cost Girth (NCG). The NCG problem is applied in many optimization issues such as scheduling and model verification. The existing polynomial algorithms suffer from high computation and memory consumption. In this paper, a powerful Map-Reduce framework implemented to find the NCG of a graph. The proposed algorithm runs in O(log k) parallel time over O(n3) on each Hadoop nodes, where n; k are the size of the graph and the value of NCG, respectively. The Hadoop implementation of the algorithm shows that the total execution time is reduced by 50% compared with polynomial algorithms, especially in large networks concerning increasing the numbers of Hadoop nodes. The result proves the efficiency of the approach for solving the NCG problem to process big data in a parallel and distributed way.

A Barotropic Solver for High-Resolution Ocean General Circulation Models

High-resolution global ocean general circulation models (OGCMs) play a key role in accurate ocean forecasting. However, the models of the operational forecasting systems are still not in high resolution due to the subsequent high demand for large computation, as well as the low parallel efficiency barrier. Good scalability is an important index of parallel efficiency and is still a challenge for OGCMs. We found that the communication cost in a barotropic solver, namely, the preconditioned conjugate gradient (PCG) method, is the key bottleneck for scalability due to the high frequency of the global reductions. In this work, we developed a new algorithm—a communication-avoiding Krylov subspace method with a PCG (CA-PCG)—to improve scalability and then applied it to the Nucleus for European Modelling of the Ocean (NEMO) as an example. For PCG, inner product operations with global communication were needed in every iteration, while for CA-PCG, inner product operations were only needed every eight iterations. Therefore, the global communication cost decreased from more than 94.5% of the total execution time with PCG to less than 63.4% with CA-PCG. As a result, the execution time of the barotropic modes decreased from more than 17,000 s with PCG to less than 6000 s with CA-PCG, and the total execution time decreased from more than 18,000 s with PCG to less than 6200 s with CA-PCG. Besides, the ratio of the speedup can also be increased from 3.7 to 4.6. In summary, the high process count scalability when using CA-PCG was effectively improved from that using the PCG method, providing a highly effective solution for accurate ocean simulation.

Accelerated execution via eager-release of dependencies in task-based workflows

Task-based programming models offer a flexible way to express the unstructured parallelism patterns of nowadays complex applications. This expressive capability is required to achieve maximum possible performance for applications that are executed in distributed execution platforms. In current task-based workflows, tasks are launched for execution when their data dependencies are satisfied. However, even though the data dependencies of a certain task might have been already produced, the execution of this task will be delayed until its predecessor tasks completely finish their execution. As a consequence of this approach of releasing dependencies, the amount of parallelism inherent in applications is limited and performance improvement opportunities are wasted. To mitigate this limitation, we propose an eager approach for releasing data dependencies. Following this approach, the execution of tasks will not be delayed until their predecessor tasks completely finish their execution, instead, tasks will be launched for execution as soon as their data requirements are available. Hence, more parallelism is exposed and applications can achieve higher levels of performance by overlapping the execution of tasks. Towards achieving this goal, in this paper we propose applying two changes to task-based workflow systems. First, modifying the dependency relationships of tasks to be specified not only in terms of predecessor and successor tasks but also in terms of the data that caused these dependencies. Second, triggering the release of dependencies as soon as a predecessor task generates the output data instead of having to wait until the end of the predecessor execution to release all of its dependencies. We realize this proposal using PyCOMPSs: a task-based programming model for parallelizing Python applications. Our experiments show that using an eager approach for releasing dependencies achieves more than 50% performance improvement in the total execution time as compared to the default approach of releasing dependencies.

Optimizations for filter-based join algorithms in MapReduce

Large-scale datasets collected from heterogeneous sources often require a join operation to extract valuable information. MapReduce is an efficient programming model for processing large-scale data. However, it has some limitations in processing heterogeneous datasets. This is because of the large amount of redundant intermediate records that are transferred through the network. Several filtering techniques have been developed to improve the join performance, but they require multiple MapReduce jobs to process the input datasets. To address this issue, the adaptive filter-based join algorithms are presented in this paper. Specifically, three join algorithms are introduced to perform the processes of filters creation and redundant records elimination within a single MapReduce job. A cost analysis of the introduced join algorithms shows that the I/O cost is reduced compared to the state-of-the-art filter-based join algorithms. The performance of the join algorithms was evaluated in terms of the total execution time and the total amount of I/O data transferred. The experimental results show that the adaptive Bloom join, semi-adaptive intersection Bloom join, and adaptive intersection Bloom join decrease the total execution time by 30%, 25%, and 35%, respectively; and reduce the total amount of I/O data transferred by 18%, 25%, and 50%, respectively.

Adaptive Granularity Based Last-Level Cache Prefetching Method with eDRAM Prefetch Buffer for Graph Processing Applications

The emergence of big data processing and machine learning has triggered the exponential growth of the working set sizes of applications. In addition, several modern applications are memory intensive with irregular memory access patterns. Therefore, we propose the concept of adaptive granularities to develop a prefetching methodology for analyzing memory access patterns based on a wider granularity concept that entails both cache lines and page granularity. The proposed prefetching module resides in the last-level cache (LLC) to handle the large working set of memory-intensive workloads. Additionally, to support memory access streams with variable intervals, we introduced an embedded-DRAM based LLC prefetch buffer that consists of three granularity-based prefetch engines and an access history table. By adaptively changing the granularity window for analyzing memory streams, the proposed model can swiftly and appropriately determine the stride of memory addresses to generate hidden delta chains from irregular memory access patterns. The proposed model achieves 18% and 15% improvements in terms of energy consumption and execution time compared to global history buffer and best offset prefetchers, respectively. In addition, our model reduced the total execution time and energy consumption by approximately 6% and 2.3%, compared to those of the Markov prefetcher and variable-length delta prefetcher.

IMPLEMENTATION OF THE SYMMETRICAL ENCRYPTION STANDARD DES USING C PROGRAMMING LANGUAGE AND COMPARISON ITS EXECUTION TIME WITH OTHER UTILITIES

This research dedicated to the review, implementation and analysis of the symmetric encryption algorithm, namely - DES (Data Encryption Standard) that encrypts and decrypts text information. For this algorithm represented not only a verbal description, but also schemes of its execution and examples of implementation. Intermediate results and the results of information encryption / decryption in the implemented algorithm were verified using examples, so we can assume that the algorithm implemented correctly. Comparison of the execution time for the DES algorithm proposed implementation made for two utilities. One of them is OpenSSL, developed using assembler and the capabilities of the C programming language. The other utility developed using programming language Java. The comparison was made according to three criteria: full time from the utility execution start to its completion; the time spent by the process to execute the utility (downtime and time when the processor perform other tasks not accounted); the time taken by the operating system to run a utility, such as reading or writing the file. The analysis showed that the total execution time is not equal to the total amount of time spent by both the processor and the operating system to execute the utilities. This is due to the following: the total execution time is the real time spent on the execution of the utility; it can measure with a stopwatch. Whereas the time spent by the processor to execute the utility is measured somewhat differently: if two cores execute the same utility for 1 second, the total execution time will be 2 seconds, although in fact one second of time has passed. From the comparison follows the next conclusion: the time spent on encryption is less than the time spent on decryption. The execution time for different utilities is different: the time for OpenSSL utility turned out to be the best, because such an implementation is most adapted to the hardware. The utility in Java turned out to be the worst in terms of execution time. We propose the implementation of the DES algorithm of the nearest execution time to the fastest of the considered. Because a number of hacking possibilities have been found for the symmetric encryption standard DES, in particular due to the small number of possible keys, there is a risk of overriding them. Therefore, to increase crypto currency, other versions of this algorithm have been developed: double DES (2DES), triple DES (3DES), DESX, G-DES. In the future, it is planned to develop a utility based on our proposed implementation of the DES algorithm and to demonstrate the operation of its modifications.