Optimasi Pengunduhan Anime Jepang Bersubtitle Indonesia dengan Metode Restful API dan Firebase Cloud Messaging

(RESTful API / REST API merupakan penerapan dari API (Application Programming Interface). Sedangkan REST (Representional State Transfer) adalah sebuah arsitektur metode komunikasi yang menggunakan protokol HTTP untuk pertukaran data dimana metode ini sering diterapkan dalam pengembangan aplikasi. Dengan tujuannya untuk menjadikan sistem memiliki performa yang baik, cepat dan mudah untuk di kembangkan (scale) terutama dalam pertukaran dan komunikasi data. Firebase Cloud Message (FCM) adalah adalah solusi pertukaran pesan lintas platform yang dapat digunakan untuk mengirim pesan tanpa biaya. Dengan FCM, Anda dapat memberi tahu aplikasi klien bahwa pesan baru atau data lainnya tersedia untuk disinkronkan. Penelitian pengembangan system menggunakan metode Prototype, metode ini cocok digunakan untuk mengembangkan sebuah perangkat lunak yang dikembangkan kembali. Metode ini membuat sebuah rancangan kilat yang selanjutnya akan dievaluasi kembali sebelum di produksi secara benar. Hasil penelitian menunjukan bahwa Aplikasi dengan menggunakan firebase cloud message dapat membantu pengguna dalam mendapatkan kabar atau pembaruan yang sedang terjadi

Hardware Parallelization of Cores Accessing Memory with Irregular Access Patterns

This project studies FPGA-based heterogeneous computing architectures with the objective of discovering their ability to optimize the performances of algorithms characterized by irregular memory access patterns. The example used to achieve this is a graph algorithm known as Triad Census Algorithm, whose implementation has been developed and tested. First of all, the triad census algorithm is presented, explaining the possible variants and reviewing the existing implementations upon different architectures. The analysis focuses on the parallelization techniques which have allowed to boost performance, thus reducing execution time. Besides, the study tackles the OpenCL programming model, the standard used to develop the final application. Special attention is paid to the language details that have motivated some of the most important design decisions. The dissertation continues with the description of the project implementation, including the application objectives, the system design, and the different variants developed to enhance algorithm performance. Finally, some of the experimental results are presented and discussed. All implemented versions are evaluated and compared to decide which is the best in terms of scalability and execution time

NeuroFlow: A General Purpose Spiking Neural Network Simulation Platform using Customizable Processors

© 2016 Cheung, Schultz and Luk.NeuroFlow is a scalable spiking neural network simulation platform for off-the-shelf high performance computing systems using customizable hardware processors such as Field-Programmable Gate Arrays (FPGAs). Unlike multi-core processors and application-specific integrated circuits, the processor architecture of NeuroFlow can be redesigned and reconfigured to suit a particular simulation to deliver optimized performance, such as the degree of parallelism to employ. The compilation process supports using PyNN, a simulator-independent neural network description language, to configure the processor. NeuroFlow supports a number of commonly used current or conductance based neuronal models such as integrate-and-fire and Izhikevich models, and the spike-timing-dependent plasticity (STDP) rule for learning. A 6-FPGA system can simulate a network of up to ~600,000 neurons and can achieve a real-time performance of 400,000 neurons. Using one FPGA, NeuroFlow delivers a speedup of up to 33.6 times the speed of an 8-core processor, or 2.83 times the speed of GPU-based platforms. With high flexibility and throughput, NeuroFlow provides a viable environment for large-scale neural network simulation

Data Provenance and Management in Radio Astronomy: A Stream Computing Approach

New approaches for data provenance and data management (DPDM) are required for mega science projects like the Square Kilometer Array, characterized by extremely large data volume and intense data rates, therefore demanding innovative and highly efficient computational paradigms. In this context, we explore a stream-computing approach with the emphasis on the use of accelerators. In particular, we make use of a new generation of high performance stream-based parallelization middleware known as InfoSphere Streams. Its viability for managing and ensuring interoperability and integrity of signal processing data pipelines is demonstrated in radio astronomy. IBM InfoSphere Streams embraces the stream-computing paradigm. It is a shift from conventional data mining techniques (involving analysis of existing data from databases) towards real-time analytic processing. We discuss using InfoSphere Streams for effective DPDM in radio astronomy and propose a way in which InfoSphere Streams can be utilized for large antennae arrays. We present a case-study: the InfoSphere Streams implementation of an autocorrelating spectrometer, and using this example we discuss the advantages of the stream-computing approach and the utilization of hardware accelerators