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"ABSTRACTMolecular Dynamics (MD) is one of processes that requires High Performance Computingenvironments to complete its jobs. In the preparation of virtual screening experiments, MD is one ofthe important processes particularly for tropical countries in searching for anti-Malaria drugs. Thesearch for anti-Malaria has previously conducted, for example by WISDOM project utilizing 1,700CPUS. This computing infrastructure will be one of the limitation for country like Indonesia that alsoneeds in silico anti malaria compounds searching from the country medical plants. Thus findingsuitable and affordable computing environment is very important. Our previous works showed that ourdedicated Cluster computing power with 16 cores performance better than those using fewer cores,however the GPU GTX family computing power is much better.In this study, we investigate further our previous experiment in finding more suitable computingenvironment on much better hardware specification of non dedicated Cluster computing and GPUTesla. We used two computing environments, the first one is Barrine HPC Cluster of The University ofQueensland which has 384 compute nodes with 3144 computing cores. The second one is Delta FutureGrid GPU Cluster which has 16 computing nodes with 192 computing cores, each nodes equippedwith 2 NVIDIA Tesla C2070 GPU (448 cores). The results show that running the experiment on adedicated computing power is much better than that on non dedicated ones, and the GPU performanceis still much better than that of Cluster."

"ABSTRACTOne of the processes requiring HPC environments is Molecular Dynamics ( MD ) . In tropical countries, the MD process is very important in the preparation of virtual screening experiments for anti-malaria search. Previous works on the virtual screening project for anti-malaria search conducted by WISDOM project uses grid infrastructure with 1,700 CPUs of various infrastructure provided in 15 countries [13]. In silico anti malaria compounds searching from Indonesian medical plants using virtual screening methods are urgently required. This can reduce the cost and time required compared to the direct searching or examining each compound by in vitro and in vivo which will spend a lot of time and expense . However, the use of thousands of processors is difficult for the researchers with limited resources in developing countries such as Indonesia. Our of previous studies using MD with GROMACS shows the improvement of the simulation time using Cluster. But that is not the case for some of our previous works with AMBER on Cluster where we did not obtain significant speed up. However, our previous works running GROMACS on GPUs provided significant speed up about 12 times faster than that run on Cluster. In this study , we build a GPU -based computing environment and have some MD simulation with AMBER. We used several computing environments such as cluster with 16 cores , GPU Geforce GTX 465 , GTX 470 , GTX 560 , GTX 680 , and GTX 780 . In addition to PfENR ( Plasmodium falciparum Enoyl acyl Carrier Protein Reductase ) enzyme , as benchmark we also conducted MD experiments on Myoglobin protein , Dihydrofolate reductase (DHFR) protein, and Ras - Raf protein . All experimental results showed that the slowest MD processes occurred on Cluster, followed in increasing order by GTX 560, GTX 465, GTX 470, GTX 680 and GTX 780. While the GPU speed up relative to cluster is about 24 , 26 , 32 , 24 , 77 and 101, respectively. "

"Molecular dynamic simulation is one field of science that uses computer as a resource for computational methods to calculate the number of forces acting within a molecular system and analyzing its movement. This simulation is useful for the discovery of drug compounds from an illness. This study uses Gromacs as molecular dynamics application which is running on cluster computing environment. A significant speed up is obtained during the experiments. "

"The invention of graphical processing units (GPUs) has significantly improved the speed of long processes used in molecular dynamics (MD) to search for drug candidates to treat diseases, such as malaria. Previous work using a single GTX GPU showed considerable improvement compared to GPUs run in a cluster environment. In the current work, AMBER and dual GTX 780 and 970 GPUs were used to run an MD simulation on the Plasmodium falciparum enoyl-acyl carrier protein reductase enzyme; the results showed that performance was improved, particularly for molecules with a large number of atoms using single GPU."

"Penelitian ini menganalisis dampak dari penggunaan Virtual Machine (VM), container, dan bare-metal terhadap performa Graphics Processing Unit (GPU) dengan memanfaatkan VM pada OpenStack Nova, container pada OpenStack Zun, dan bare-metal pada OpenStack Ironic. Metode virtualisasi GPU yang digunakan pada penelitian ini adalah GPU passthrough. Pengukuran performa GPU dilakukan dengan menggunakan aplikasi Glmark2 untuk menguji performa graphic rendering, Phoronix NAMD untuk menguji performa simulasi molekuler, dan Phoronix PyTorch untuk menguji performa training model. Hasil analisis menunjukkan bahwa penggunaan VM pada OpenStack Nova mengakibatkan penurunan performa GPU sebesar 15.5% pada Glmark2, 44.0% pada Phoronix NAMD, dan 8.4% pada Phoronix PyTorch. Penggunaan container pada Open Stack Zun mengakibatkan penurunan performa GPU sebesar 5.8% pada Glmark2 dan 19.7% pada Phoronix NAMD, tetapi tak ada perbedaan signifikan pada Phoronix PyTorch jika dibandingkan dengan physical machine (α = 0.05). Penggunaan bare-metal pada OpenStack Ironic mengakibatkan penurunan performa sebesar 1.5% pada Phoronix NAMD dan peningkatan tak signifikan sebesar -6.2% pada Phoronix PyTorch. Pengujian Glmark2 pada OpenStack Ironic dengan perlakuan yang sama seperti benchmark lainnya menunjukkan adanya penurunan performa sebesar 8.7%. Namun, perlakuan khusus pada Glmark2 OpenStack Ironic menunjukkan peningkatan performa sebesar -1.0% pada resolusi 1920x1080 jika dibandingkan dengan physical machine. Perlakuan khusus ini berupa menjalankan dummy Glmark2 dengan resolusi yang sangat rendah dan Glmark2 utama secara bersamaan. Berdasarkan hasil penelitian, dapat disimpulkan bahwa urutan computing resource dengan penurunan performa GPU yang paling minimal adalah penggunaan bare-metal OpenStack Ironic, diikuti dengan penggunaan container OpenStack Zun, dan diikuti dengan penggunaan VM OpenStack Nova.

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This research analyzes the effects of Virtual Machine (VM), containers, and bare-metal usage on Graphics Processing Unit (GPU) performance, using VMs provided by OpenStack Nova, containers provided by OpenStack Zun, and bare-metal provided by OpenStack Ironic. The GPU virtualization method employed in this paper is GPU passthrough. GPU performance is measured using multiple benchmark applications, those being Glmark2 to measure graphic rendering performance, Phoronix NAMD to measure molecular simulation performance, and Phoronix PyTorch to measure training model performance. The results of our analysis shows that the usage of OpenStack Nova’s VMs causes GPU performance slowdown of up to 15.5% on Glmark2, 44.0% on Phoronix NAMD and 8.4% on Phoronix PyTorch. Using OpenStack Zun’s containers also causes GPU performance slowdowns of up to 5.8% on Glmark2 and 19.7% on Phoronix NAMD, with no significant changes on GPU performance with Phoronix PyTorch compared to the physical machine (α = 0.05). In contrast, using OpenStack Ironic’s bare-metal causes GPU performance slowdown of 1.5% on Phoronix NAMD and an insignificant increase in performance on Phoronix PyTorch by 6.2%. Meanwhile the results of the Glmark2 benchmark on OpenStack Ironic following the normal procedures shows GPU performance slowdown of up to 8.7%. However, the same Glmark2 OpenStack Ironic benchmark with a special procedure shows an increase in GPU performance of up to 1.0% on the 1920x1080 resolution compared to the physical machine. This special procedure involves running a dummy Glmark2 process with a tiny resolution in parallel with the main Glmark2 process. Based on the results, we can conclude that the hierarchy of computing resources in terms of minimal GPU performance slowdown starts with the usage of OpenStack Ironic’s bare-metal, followed by the usage of OpenStack Zun’s containers, and lastly the usage of OpenStack Nova’s VMs."

"Penelitian ini menganalisis dampak dari penggunaan Virtual Machine (VM), container, dan bare-metal terhadap performa Graphics Processing Unit (GPU) dengan memanfaatkan VM pada OpenStack Nova, container pada OpenStack Zun, dan bare-metal pada OpenStack Ironic. Metode virtualisasi GPU yang digunakan pada penelitian ini adalah GPU passthrough. Pengukuran performa GPU dilakukan dengan menggunakan aplikasi Glmark2 untuk menguji performa graphic rendering, Phoronix NAMD untuk menguji performa simulasi molekuler, dan Phoronix PyTorch untuk menguji performa training model. Hasil analisis menunjukkan bahwa penggunaan VM pada OpenStack Nova mengakibatkan penurunan performa GPU sebesar 15.5% pada Glmark2, 44.0% pada Phoronix NAMD, dan 8.4% pada Phoronix PyTorch. Penggunaan container pada Open Stack Zun mengakibatkan penurunan performa GPU sebesar 5.8% pada Glmark2 dan 19.7% pada Phoronix NAMD, tetapi tak ada perbedaan signifikan pada Phoronix PyTorch jika dibandingkan dengan physical machine (α = 0.05). Penggunaan bare-metal pada OpenStack Ironic mengakibatkan penurunan performa sebesar 1.5% pada Phoronix NAMD dan peningkatan tak signifikan sebesar -6.2% pada Phoronix PyTorch. Pengujian Glmark2 pada OpenStack Ironic dengan perlakuan yang sama seperti benchmark lainnya menunjukkan adanya penurunan performa sebesar 8.7%. Namun, perlakuan khusus pada Glmark2 OpenStack Ironic menunjukkan peningkatan performa sebesar -1.0% pada resolusi 1920x1080 jika dibandingkan dengan physical machine. Perlakuan khusus ini berupa menjalankan dummy Glmark2 dengan resolusi yang sangat rendah dan Glmark2 utama secara bersamaan. Berdasarkan hasil penelitian, dapat disimpulkan bahwa urutan computing resource dengan penurunan performa GPU yang paling minimal adalah penggunaan bare-metal OpenStack Ironic, diikuti dengan penggunaan container OpenStack Zun, dan diikuti dengan penggunaan VM OpenStack Nova.

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This research analyzes the effects of Virtual Machine (VM), containers, and bare-metal usage on Graphics Processing Unit (GPU) performance, using VMs provided by OpenStack Nova, containers provided by OpenStack Zun, and bare-metal provided by OpenStack Ironic. The GPU virtualization method employed in this paper is GPU passthrough. GPU performance is measured using multiple benchmark applications, those being Glmark2 to measure graphic rendering performance, Phoronix NAMD to measure molecular simulation performance, and Phoronix PyTorch to measure training model performance. The results of our analysis shows that the usage of OpenStack Nova’s VMs causes GPU performance slowdown of up to 15.5% on Glmark2, 44.0% on Phoronix NAMD and 8.4% on Phoronix PyTorch. Using OpenStack Zun’s containers also causes GPU performance slowdowns of up to 5.8% on Glmark2 and 19.7% on Phoronix NAMD, with no significant changes on GPU performance with Phoronix PyTorch compared to the physical machine (α = 0.05). In contrast, using OpenStack Ironic’s bare-metal causes GPU performance slowdown of 1.5% on Phoronix NAMD and an insignificant increase in performance on Phoronix PyTorch by 6.2%. Meanwhile the results of the Glmark2 benchmark on OpenStack Ironic following the normal procedures shows GPU performance slowdown of up to 8.7%. However, the same Glmark2 OpenStack Ironic benchmark with a special procedure shows an increase in GPU performance of up to 1.0% on the 1920x1080 resolution compared to the physical machine. This special procedure involves running a dummy Glmark2 process with a tiny resolution in parallel with the main Glmark2 process. Based on the results, we can conclude that the hierarchy of computing resources in terms of minimal GPU performance slowdown starts with the usage of OpenStack Ironic’s bare-metal, followed by the usage of OpenStack Zun’s containers, and lastly the usage of OpenStack Nova’s VMs."

"Penelitian ini menganalisis dampak dari penggunaan Virtual Machine (VM), container, dan bare-metal terhadap performa Graphics Processing Unit (GPU) dengan memanfaatkan VM pada OpenStack Nova, container pada OpenStack Zun, dan bare-metal pada OpenStack Ironic. Metode virtualisasi GPU yang digunakan pada penelitian ini adalah GPU passthrough. Pengukuran performa GPU dilakukan dengan menggunakan aplikasi Glmark2 untuk menguji performa graphic rendering, Phoronix NAMD untuk menguji performa simulasi molekuler, dan Phoronix PyTorch untuk menguji performa training model. Hasil analisis menunjukkan bahwa penggunaan VM pada OpenStack Nova mengakibatkan penurunan performa GPU sebesar 15.5% pada Glmark2, 44.0% pada Phoronix NAMD, dan 8.4% pada Phoronix PyTorch. Penggunaan container pada Open Stack Zun mengakibatkan penurunan performa GPU sebesar 5.8% pada Glmark2 dan 19.7% pada Phoronix NAMD, tetapi tak ada perbedaan signifikan pada Phoronix PyTorch jika dibandingkan dengan physical machine (α = 0.05). Penggunaan bare-metal pada OpenStack Ironic mengakibatkan penurunan performa sebesar 1.5% pada Phoronix NAMD dan peningkatan tak signifikan sebesar -6.2% pada Phoronix PyTorch. Pengujian Glmark2 pada OpenStack Ironic dengan perlakuan yang sama seperti benchmark lainnya menunjukkan adanya penurunan performa sebesar 8.7%. Namun, perlakuan khusus pada Glmark2 OpenStack Ironic menunjukkan peningkatan performa sebesar -1.0% pada resolusi 1920x1080 jika dibandingkan dengan physical machine. Perlakuan khusus ini berupa menjalankan dummy Glmark2 dengan resolusi yang sangat rendah dan Glmark2 utama secara bersamaan. Berdasarkan hasil penelitian, dapat disimpulkan bahwa urutan computing resource dengan penurunan performa GPU yang paling minimal adalah penggunaan bare-metal OpenStack Ironic, diikuti dengan penggunaan container OpenStack Zun, dan diikuti dengan penggunaan VM OpenStack Nova.

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This research analyzes the effects of Virtual Machine (VM), containers, and bare-metal usage on Graphics Processing Unit (GPU) performance, using VMs provided by OpenStack Nova, containers provided by OpenStack Zun, and bare-metal provided by OpenStack Ironic. The GPU virtualization method employed in this paper is GPU passthrough. GPU performance is measured using multiple benchmark applications, those being Glmark2 to measure graphic rendering performance, Phoronix NAMD to measure molecular simulation performance, and Phoronix PyTorch to measure training model performance. The results of our analysis shows that the usage of OpenStack Nova’s VMs causes GPU performance slowdown of up to 15.5% on Glmark2, 44.0% on Phoronix NAMD and 8.4% on Phoronix PyTorch. Using OpenStack Zun’s containers also causes GPU performance slowdowns of up to 5.8% on Glmark2 and 19.7% on Phoronix NAMD, with no significant changes on GPU performance with Phoronix PyTorch compared to the physical machine (α = 0.05). In contrast, using OpenStack Ironic’s bare-metal causes GPU performance slowdown of 1.5% on Phoronix NAMD and an insignificant increase in performance on Phoronix PyTorch by 6.2%. Meanwhile the results of the Glmark2 benchmark on OpenStack Ironic following the normal procedures shows GPU performance slowdown of up to 8.7%. However, the same Glmark2 OpenStack Ironic benchmark with a special procedure shows an increase in GPU performance of up to 1.0% on the 1920x1080 resolution compared to the physical machine. This special procedure involves running a dummy Glmark2 process with a tiny resolution in parallel with the main Glmark2 process. Based on the results, we can conclude that the hierarchy of computing resources in terms of minimal GPU performance slowdown starts with the usage of OpenStack Ironic’s bare-metal, followed by the usage of OpenStack Zun’s containers, and lastly the usage of OpenStack Nova’s VMs."

"ABSTRAKTeknologi di bidang perangkat lunak dan perangkat keras semakin berkembang cepat. Masalah keterbatasan kapasitas suatu komputer memicu berkembangnya sebuah inovasi yang disebut dengan High Performance Computing HPC . HPC merupakan sekumpulan komputer yang digabungkan dalam sebuah jaringan dan dikoordinasi oleh software khusus. Cloud Computing merupakan paradigma yang relatif baru dalam bidang komputasi. Pada penelitian ini dilakukan pengujian terhadap performansi High Performance Computing Cluster HPCC berbasis cloud menggunakan layanan OpenStack dalam menjalankan fungsi dasar Message Passing Interface. Pengujian dilakukan menggunakan program Mpptest dan SIMPLE-O. Penggunaan server yang tidak mendukung hypervisor KVM pada pengujian point-to-point communication dapat menurunkan performansi HPCC berbasis cloud sebesar 3,1 - 12,4 dibandingkan dengan HPCC berbasis non-cloud. Pada pengujian point-to-point communication dengan 2 server yang mendukung hypervisor KVM, HPCC berbasis cloud unggul dibandingkan HPCC berbasis non-cloud sebesar 1,6 ndash; 2,7 . Pada pengujian performansi HPCC dalam melakukan fungsi MPI collective communication tidak ditemukan perbedaan berarti antara kedua cluster dimana HPCC berbasis non-cloud mengungguli HPCC berbasis cloud sebesar 0 - 1,4 . Pada pengujian menggunakan program SIMPLE-O didapati performansi HPCC berbasis cloud dan non-cloud imbang jika semua instance dijalankan dengan server yang mendukung hypervisor KVM, apabila terdapat instance yang dijalankan server tanpa dukungan KVM maka HPCC berbasis non-cloud unggul 96,2 dibandingkan HPCC berbasis cloud. Ketersedian modul KVM pada server yang menjadi host suatu instance sangat berpengaruh terhadap performansi HPCC berbasis cloud.

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ABSTRACTSoftware and hardware technologies have been developing rapidly. Capacity limation problems found in computers triggered a development of a new innovation called High Performance Computing HPC . HPC is a cluster of computers in a network coordinated by a special software. Cloud Computing is a new paradigm in computation field. In this research, series of test are done to find out the performance of cloud and non cloud based High Performance Computing Cluster HPCC while running basic functions of Message Passing Interface. Tests are done using Mpptest and SIMPLE O program. By using a server that does not support KVM in point to point communication test could decrease the performance of cloud based HPCC by 3,1 to 12,4 compared to non cloud based HPCC. During the test of point to point communication using 2 servers that support KVM hypervisor, cloud based HPCC is ahead of non cloud based HPCC by 1,6 to 2,7 . During the test of collective communication, there are no significant differences between performances of the two cluster, with non cloud based HPCC is ahead by 0 to 1,4 compared to cloud based HPCC. During the test using SIMPLE O program, the two cluster is even in term of performance as long as every instance is run by servers that support KVM hypervisor, if there is an instance that is run by a server that does not support KVM hypervisor then the performance of non cloud based HPCC is still ahead by 96,2 compared to cloud based HPCC. During the performance testing of HPCC while running collective communication, noticable performance difference between cloud and non cloud based HPCC was not found. The availability of KVM module in a server that is used to host an instance is really essential to the cloud based HPCC performance."