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"Improvement in this experiment are done for 3 following factors: running time, memory efficiency, and speedup. The speedup result achieved is as close as 100× increase. Naïve parallelization is used on mapping each matrices data to CUDA memories, for each major operation is done in parallel behavior via self-made CUDA kernels to suits the data dimensions. This make up the improvement of 2nd factor, which is memory efficiency. Results for kernels are captured with NVIDIA profiling tools for the increasing number of random targets on 4 transmitter-receiver (PV) combinations (without any knowledge about the approximation of targets direction). All results are taken according to the average running time of kernel calls and speed up for each size of the input, compared with serial and CPU parallel version data of the previous work. Among advanced techniques for the passive radar system’s target association, several experiments have been done based on Probability Hypothetic Density (PHD) function. The complex calculation makes the computation processes a very demanding task to be done, thus, this paper is focused on PHD function performance comparison between preceding attempts to the implementation using a pure C programming language with CUDA library. A further improvement is highly possible within algorithm optimization itself or applying more advanced parallelization technique.

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Peningkatan yang dilakukan pada eksperimen ini meliputi 3 faktor: running time, memory efficiency, dan speedup. Hasil pengujian speedup yang diperoleh mencapai setidaknya 100x peningkatan daripada algoritma semula. Paralelisasi naif yang digunakan untuk memetakan setiap matriks data ke dalam memori CUDA, untuk setiap operasi major dilakukan secara paralel dengan CUDA kernel yang didesain mandiri sehingga dapat menyesuaikan secara otomatis dengan dimensi data yang digunakan. Hal ini memungkinkan peningkatan pada faktor yang kedua yaitu memory efficiency. Hasil dari masing-masing kernel diukur menggunakan data yang diambil dari NVIDIA profiling tools untuk data acak yang meningkat dari segi ukuran, dan diimplementasikan pada 4 kombinasi transmitter-reveiver (PV) tanpa mengetahui aproksimasi arah target. Seluruh hasil pengujian kernel diambil berdasarkan rata-rata running time dari pemanggilan kernel dan speed up dari setiap ukuran masukan, dibandingkan dengan implementasi asosiasi target secara serial dan versi paralel pada CPU dari penelitian terdahulu. Diantara teknik tingkat lanjut yang digunakan untuk menentukan asosiasi target pada sistem radar pasif, beberapa percobaan telah dilakukan berdasarkan fungsi Probability Hypothetic Density (PHD). Kalkulasi yang kompleks menghasilkan proses komputasi yang terlalu berat untuk dilakukan, maka dari itu, percobaan ini fokus kepada komparasi performa fungsi PHD antara penelitian-penelitian terdahulu dengan impleentasi fungsi tersebut pada pustaka CUDA menggunakan bahasa pemrograman C. Peningkatan lebih lanjut sangat dimungkinkan melalui optimisasi algoritma PHD sendiri atau menggunakan teknik paralelisasi yang lebih baik.

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"ABSTRAKSistem radar untuk aplikasi medis merupakan telah banyak diteliti dan dikembangkan. Salah satu aplikasinya adalah pengukuran kondisi vital manusia seperti tingkat pernafasan dan tingkat detak jantung. Oleh karena itu, diperlukan suatu sistem yang dapat mendeteksi tingkat pernafasan manusia dan tingkat detak jantung manusia. Dalam penelitian ini, dirancang radar continuous-wave dengan LoRa RFM95 sebagai transmitter pada frekuensi kerja 862-1020 MHz dan RTL-SDR sebagai receiver sekaligus analog to digital converter. Antena yang digunakan dalam penelitian ini adalah antena microstrip rectangular satu elemen sebanyak dua buah, masing-masing sebagai antena pengirim dan antena penerima dengan frekuensi tengah 904 MHz dan bandwidth 2,8 dengan gain -1,936 dBi. Melalui persamaan umum radar, dihitung jarak maksimum radar untuk deteksi tingkat pernafasan manusia adalah sebesar 2,002 meter dan untuk deteksi tingkat detak jantung manusia adalah sebesar 0,8954 meter. Pengambilan data dilakukan selama 60 detik tiap pengambilan yang dibagi dalam delapan skenario, yaitu skenario ketika transmitter tidak diaktifkan, skenario ketika tidak ada target, skenario ketika target bernafas normal, skenario ketika target bernafas dalam, skenario target meninggalkan jangkauan radar, skenario target mengayunkan tangan, skenario target bergerak mendekati dan menjauhi radar, dan skenario target selesai berolahraga. Jarak antara target dengan sistem radar adalah sejauh 0,7 meter. Metode yang digunakan untuk mendapatkan tingkat pernafasan dan detak jantung manusia adalah metode sampling langsung, demodulasi amplitudo, dan demodulasi arctangent. Demodulasi amplitudo memiliki performa paling baik dibandingkan dengan metode yang lain. Dengan metode demodulasi amplitudo, sistem radar ini dapat mendeteksi tingkat pernafasan manusia, tetapi belum mampu mendeteksi tingkat detak jantung manusia karena noise dan atenuasi yang besar.

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ABSTRACTRadar systems for medical applications are widely researched and developed. One application of this radar is to measure human vital conditions such as respiratory rate and heartbeat rate. Therefore, a system that can detect human respiratory rate and human heartbeat rate is in need. In this study, a continuous-wave radar was designed with a LoRa RFM95 as a transmitter at 862-1020 MHz frequency and RTL-SDR as a receiver as well as an analog to digital converter. The antenna used in this study are two single elements rectangular microstrip patch antennas, each for transmitting antenna and for receiving antenna with center frequency of 904 MHz, bandwidth of 2.8, and gain of 1.936 dBi. Using radar range equation, the maximum radar distance to detect humans respiratory rate is 2.002 meters and the maximum radar distance to detect humans heartbeat rate is 0.8954 meters. Data is collected for 60 seconds for each batch and is divided into eight scenarios, namely the scenario when the transmitter is not activated, the scenario when there is no target, the scenario when the target breathes normally, the scenario when the target breathes deeply, the scenario when target leaves radar reach, the scenario when target swings his her arm, the scenario when target moves forward and backward, and the scenario when target has finished excercising. The distance between the target and the radar system is 0.7 meters. The methods used to obtain human respiratory rate and heartbeat rate are direct sampling method, amplitude demodulation, and arctangent demodulation. Amplitude demodulation has the best performance compared to other methods. With amplitude demodulation method, this radar system can detect human respiratory rates, but has not been able to detect the human heartbeat rate due to the presence of noise and attenuation."

"This monograph presents examples of best practices when combining bioinspired algorithms with parallel architectures. The book includes recent work by leading researchers in the field and offers a map with the main paths already explored and new ways towards the future."

"Tingginya tingkat kompleksitas program menyebabkan program memiliki waktu eksekusi yang lama jikalau tidak dijalankan pada mesin berkomputasi tinggi. Masalah ini dapat diatasi salah satunya dengan cara menjalankan berbagai proses pada program tersebut secara simultan sehingga program dapat semakin cepat tereksekusi. Metode ini dikenal dengan istilah parallel computing. Untuk lebih mempercepat waktu eksekusi program, parallel computing tersebut dapat diimplementasikan pada arsitektur High Performance Computing HPC. Metode parallel computing dalam HPC tersebut diimplementasikan ke dalam program Sistem Penilaian Esai Otomatis Simple-O. Simple-O merupakan program penilai esai otomatis yang merupakan pengembangan dari Departemen Teknik Elektro. Dengan menerapkan parallel computing dan menjalankan program pada HPC, eksekusi yang dibutuhkan untuk memeriksa jawaban esai dapat semakin cepat. Parallel computing atau parallelism akan diterapkan pada salah satu bagian dari Simple-O yaitu pada algoritma pembelajaran dalam Simple-O, lebih tepatnya pada Self Organizing Map SOM atau Learning Vector Quantization LVQ. Parallelism dalam SOM dan LVQ diterapkan dengan metode network partition dimana node komputasi Euclidean distance dilakukan secara parallel. Pada penelitian ini didapatkan bahwa kecepatan program serial 1,28 kali lebih cepat dibandingkan program parallel.

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The escalation of program complexity nowadays means slower run time when it is not executed in high performance machine. One way to address this issue is to execute the processes in the program simultaneously so the program may be executed quicker, known as parallel computing. To further accelerate the program parallel computing can be implemented in High Performance Computing HPC architecture. This method of applicating parallel computing with HPC is implemented in Automatic Essay Grading System, known as Simple O. Simple O is an automatic essay grading system developed by Department of Electrical Engineering Universitas Indonesia. The purpose of applicating the aforementioned method to Simple O is to accelerate the speed of essay grading execution. Parallel computing will be implemented to one of Simple O rsquo s part of program, which is in the learning algorithm. The learning algorithm applied in Simple O is Self Organizing Map SOM and Learning Vector Quantization LVQ. The implementation of parallelism in the learning algorithm uses network partition method, where the calculation of Euclidean distance is done in parallel. Through this research, it can be concluded that the the speed of serial program is 1.28 times quicker than the parallelized program."

"Scientific computing has often been called the third approach to scientific discovery, emerging as a peer to experimentation and theory. Historically, the synergy between experimentation and theory has been well understood: experiments give insight into possible theories, theories inspire experiments, experiments reinforce or invalidate theories, and so on. As scientific computing has evolved to produce results that meet or exceed the quality of experimental and theoretical results, it has become indispensable.Parallel processing has been an enabling technology in scientific computing for more than 20 years. This book is the first in-depth discussion of parallel computing in 10 years; it reflects the mix of topics that mathematicians, computer scientists, and computational scientists focus on to make parallel processing effective for scientific problems. Presently, the impact of parallel processing on scientific computing varies greatly across disciplines, but it plays a vital role in most problem domains and is absolutely essential in many of them. Parallel Processing for Scientific Computing is divided into four parts: The first concerns performance modeling, analysis, and optimization; the second focuses on parallel algorithms and software for an array of problems common to many modeling and simulation applications; the third emphasizes tools and environments that can ease and enhance the process of application development; and the fourth provides a sampling of applications that require parallel computing for scaling to solve larger and realistic models that can advance science and engineering."

"The proceedings of the 5th International Workshop on Parallel Tools for High Performance Computing provide an overview on supportive software tools and environments in the fields of system management, parallel debugging and performance analysis. In the pursuit to maintain exponential growth for the performance of high performance computers the HPC community is currently targeting exascale systems. The initial planning for exascale already started when the first petaflop system was delivered. Many challenges need to be addressed to reach the necessary performance. Scalability, energy efficiency and fault-tolerance need to be increased by orders of magnitude. The goal can only be achieved when advanced hardware is combined with a suitable software stack. In fact, the importance of software is rapidly growing. As a result, many international projects focus on the necessary software."

"This book provides an overview of the whole radar target recognition process, and covers the key techniques being developed for operational systems. The book is based on the fundamental scientific principles of high resolution radar, and explains how the techniques can be used in real systems. Mathematics is kept to a minimum and the complex techniques and issues are discussed in a clear and physical way in order to make it accessible both to specialists and non specialists alike."

"Skripsi ini membahas mengenai sebuah rancang bangun simulasi pengolahan data radar dengan menggunakan sistem pemetaan Map server berbasiskan web. Aplikasi ini merupakan pengembangan teknologi yang ditujukan pada sistem monitoring lalu lintas udara. Pengembangan ini mempermudah kinerja sistem monitoring lalu lintas udara sehingga dapat dilakukan dengan mudah tanpa melihat batas lokasi. Dengan menggunakan pemetaan Mapserver berbasiskan web memudahkan sistem untuk dipetakan sesuai dengan standar koordinat yang terhubung pada web server dengan memanfaatkan teknologi jaringan komputer. Informasi monitoring data radar ini akan ditampilkan melalui web server dalam bentuk web yang dapat dengan mudah diakses oleh user-user tertentu.

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This essay discusses the design of a wake simulation data processing system by using radar mapping of Map-based web server. This application is the development of technology aimed at the system of monitoring air traffic. Facilitate the development of this system of monitoring the performance of air traffic so it can be done easily without limit locations.

By using mapping Map server facilitate web-based system to be in accordance with the standards of coordinates that is connected to the web server by using computer network technology. Information monitoring radar data will be displayed through a web server in the form of web that can be easily accessed by certain users."