Judul: Development of Low Cost Parallel Computing Practicum Laboratory with Open MPI and NFS
Abstrak: High performance computing (HPC) merupakan salah satu bidang penelitian paling aktif di bidang ilmu komputer dan rekayasa (engineering). HPC dikembangkan untuk memecahkan sejumlah masalah yang kompleks dalam ilmu komputasi, seperti permodelan, analisis data dan desain produk. Saat ini pembelajaran kuliah dan praktikum Mata Kuliah Komputasi Paralel (KOM 613) masih menggunakan komputer tunggal untuk mempelajari pemrograman HPC tersebut, Padahal untuk pemrograman HPC yang bersifat distributed memory idealnya menggunakan komputer klaster. Penelitian ini bertujuan untuk membuat Laboratorium Komputer yang dapat mendukung pembelajaran pemrograman HPC. Laboratorium Komputer menggunakan beberapa komputer (multicomputer). Setiap komputer terbagi dalam beberapa peran, yaitu head node, file server dan compute node. Head node sebagai kontrol utama, compute node menjalankan program yang dibagikan, dan file server menjadi penyimpan file. Seluruh komputer disambungkan secara fisik melalui local area network. Metode penelitian yang dilakukan meliputi analisis, perancangan, implementasi dan evaluasi. Pengukuran kinerja komputasi dan komunikasi dilakukan terhadap komputer klaster dengan menggunakan perangkat lunak benchmark High Performance Linpack dan mpiBench, dan menghasilkan kinerja sebesar 21 GFLOPS dan 473 ms. Berdasarkan hasil benchmark, kemampuan komputer klaster dapat ditingkatkan dengan menggunakan komputer dan perangkat keras jaringan yang lebih baik dan sebaiknya juga digunakan job scheduler untuk memudahkan pengguna dalam menggunakan komputer klaster., High performance computing (HPC) is one of the most active research areas in computer science and engineering. HPC was developed to solve a number of complex problems in computational science, such as modeling, data analysis and product design. Currently lectures and practicum courses for Parallel Computing Course (KOM 613) still use a single computer to learn the HPC programming, whereas for HPC programming which is distributed memory in nature it is ideal to use a cluster computer. This study aims to create a Computer Laboratory that can support HPC programming learning. Computer Laboratory uses several computers (multicomputer). Each computer is divided into several roles, namely head node, file server and compute node. The head node is the main control, the compute node is running the shared program, and the file server is the file repository. All computers are physically connected via a local area network. The research method used includes analysis, design, implementation and evaluation. Computing and communication performance measurements were performed on cluster computers using the High Performance Linpack and mpiBench benchmark software, and resulted in a performance of 21 GFLOPS and 473 ms. Based on the benchmark results, the ability of cluster computers can be improved by using better computers and network hardware and a job scheduler should also be used to make it easier for users to use cluster computers.
Keyword: Benchmark, High Performance Computer, Cluster Computer, Computer Labs, Open MPI

Judul: Pengembangan Sistem Pengukur Indeks Beban untuk Sistem Penyeimbang Beban pada Komputer Paralel Cluster
Abstrak: The performance of a parallel system can be improved in various ways; one of them is load balancing. To perform load balancing task, some load balancing algorithms require the information of load index on each slave computer. This research developed a system to read the load index on each slave computer of a computer cluster that can measure, send, and manage information of load value. The application was implemented using Hyperic SIGAR (System Information Gatherer And Reporter) with CPU, memory, and I/O as the load indices. Analysis of the system performance testing showed that throughput values are proportional to the number of slave computers and inversely proportional to delay. Meanwhile, analysis of load value validity showed that load value is inversely proportional to the delivery delay and the number of slave computer. However, there were several anomalies in the delta analysis due to several factors, such as the unisolated network during the testing period.
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Judul: Revisi Marga Maotia Wedd. (Urticacae) di Malesia
Abstrak: A taxonomic revision of genus Maozrtia in Malesia has been done. At present revision, 6 species are recognized namely M. ambigua, M. diversifolia, M. lanceolata, M. platistigma, M puya, and M. setosa. M ambigua is divided into 7 varieties, which one of the variety (i.e. var. tomentosa) n ew to science and another (i.e. var. grandifolia) is a new combination based on M. grandifolia. Two new varieties w i t h M. diversivolia (namely var. novoguinesis and var. suniatrana) are proposed. M. gracilis is recognized here as a synonim 0f M. lanceolata.
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