MEGADOCK 3.0: a high-performance protein-protein interaction prediction software using hybrid parallel computing for petascale supercomputing environments

Matsuzaki, Yasushi; Uchikoga, Nobuyuki; Ohue, Masahito; Shimoda, Takehiro; Sato, Toshiyuki; Ishida, Takashi; Akiyama, Yutaka

doi:10.1186/1751-0473-8-18

Cited by 25 publications

(16 citation statements)

References 13 publications

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“…This slight decrease was due to hyper-threading, which dropped the efficiency of thread execution when multiple threads were assigned to a single physical core. A similar behavior can be found in other memory-intensive applications [15]- [17].…”

Section: Fig 10supporting

confidence: 80%

Accelerating the Held-Karp Algorithm for the Symmetric Traveling Salesman Problem

Kimura

Higa

Okita

et al. 2019

IEICE Trans. Inf. & Syst.

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In this paper, we propose an acceleration method for the Held-Karp algorithm that solves the symmetric traveling salesman problem by dynamic programming. The proposed method achieves acceleration with two techniques. First, we locate data-independent subproblems so that the subproblems can be solved in parallel. Second, we reduce the number of subproblems by a meet in the middle (MITM) technique, which computes the optimal path from both clockwise and counterclockwise directions. We show theoretical analysis on the impact of MITM in terms of the time and space complexities. In experiments, we compared the proposed method with a previous method running on a single-core CPU. Experimental results show that the proposed method on an 8-core CPU was 9.5-10.5 times faster than the previous method on a single-core CPU. Moreover, the proposed method on a graphics processing unit (GPU) was 30-40 times faster than that on an 8-core CPU. As a side effect, the proposed method reduced the memory usage by 48%. key words: symmetric traveling salesman problem, Held-Karp algorithm, parallelization, meet in the middle, GPU

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Section: Fig 10supporting

confidence: 80%

Accelerating the Held-Karp Algorithm for the Symmetric Traveling Salesman Problem

Kimura

Higa

Okita

et al. 2019

IEICE Trans. Inf. & Syst.

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show abstract

“…Figure 3 andTable 4 show the total docking calculation time results for the dataset. MEGADOCK was parallelized previously using OpenMP and it provided good acceleration with multicores, as reported in our previous study [ 17 ]. With this dataset, it achieved a 6.3-fold speed up using eight CPU cores.…”

Section: Methodsmentioning

confidence: 99%

“…It calculates these functions using a single FFT calculation and is much faster than the well-known docking program ZDOCK [ 13 ], which requires eight FFT calculations. MEGADOCK has already been parallelized using MPI and OpenMP for multiple combinations of protein pairs [ 16 , 17 ].…”

Section: Megadockmentioning

confidence: 99%

Protein-protein docking on hardware accelerators: comparison of GPU and MIC architectures

et al. 2015

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BackgroundThe hardware accelerators will provide solutions to computationally complex problems in bioinformatics fields. However, the effect of acceleration depends on the nature of the application, thus selection of an appropriate accelerator requires some consideration.ResultsIn the present study, we compared the effects of acceleration using graphics processing unit (GPU) and many integrated core (MIC) on the speed of fast Fourier transform (FFT)-based protein-protein docking calculation. The GPU implementation performed the protein-protein docking calculations approximately five times faster than the MIC offload mode implementation. The MIC native mode implementation has the advantage in the implementation costs. However, the performance was worse with larger protein pairs because of memory limitations.ConclusionThe results suggest that GPU is more suitable than MIC for accelerating FFT-based protein-protein docking applications.

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“…This mechanism can synchronize multiple threads approximately ten times faster than software barrier processing. Thus, we can reduce the overhead at the level of fine-grained parallelism and obtain very good scalability of parallel threads [12].…”

Section: Performance Optimization and Evaluation On The K Computermentioning

confidence: 99%

Performance Analysis and Optimization of Nonhydrostatic ICosahedral Atmospheric Model (NICAM) on the K Computer and TSUBAME2.5

Yashiro

Terai

Yoshida

et al. 2016

Proceedings of the Platform for Advanced Scientific Computing Conference

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We summarize the optimization and performance evaluation of the Nonhydrostatic ICosahedral Atmospheric Model (NICAM) on two different types of supercomputers: the K computer and TSUBAME2.5. First, we evaluated and improved several kernels extracted from the model code on the K computer. We did not significantly change the loop and data ordering for sufficient usage of the features of the K computer, such as the hardware-aided thread barrier mechanism and the relatively high bandwidth of the memory, i.e., a 0.5 Byte/FLOP ratio. Loop optimizations and code cleaning for a reduction in memory transfer contributed to a speed-up of the model execution time. The sustained performance ratio of the main loop of the NICAM reached 0.87 PFLOPS with 81,920 nodes on the K computer. For GPUbased calculations, we applied OpenACC to the dynamical core of NICAM. The performance and scalability were evaluated using the TSUBAME2.5 supercomputer. We achieved good performance results, which showed efficient use of the memory throughput performance of the GPU as well as good weak scalability. A dry dynamical core experiment was carried out using 2560 GPUs, which achieved 60 TFLOPS of sustained performance.

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MEGADOCK 3.0: a high-performance protein-protein interaction prediction software using hybrid parallel computing for petascale supercomputing environments

Cited by 25 publications

References 13 publications

Accelerating the Held-Karp Algorithm for the Symmetric Traveling Salesman Problem

Accelerating the Held-Karp Algorithm for the Symmetric Traveling Salesman Problem

Protein-protein docking on hardware accelerators: comparison of GPU and MIC architectures

Performance Analysis and Optimization of Nonhydrostatic ICosahedral Atmospheric Model (NICAM) on the K Computer and TSUBAME2.5

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