@article {23328,
	title = {An Analytical GPU Performance Model for 3D Stencil Computations from the Angle of Data Traffic},
	journal = {The Journal of Supercomputing},
	volume = {71},
	year = {2015},
	month = {02/2015},
	pages = {2433-2453},
	publisher = {Springer},
	abstract = {The achievable GPU performance of many scientific computations is not determined by a GPU{\textquoteright}s peak floating-point rate, but rather how fast data are moved through different stages of the entire memory hierarchy. We take low-order 3D stencil computations as a representative class to study the reachable GPU performance from the angle of data traffic. Specifically, we propose a simple analytical model to estimate the execution time based on quantifying the data traffic volume at three stages: (1) between registers and on-SMX storage, (2) between on-SMX storage and L2 cache, (3) between L2 cache and GPU{\textquoteright}s device memory. Three associated granularities are used: a CUDA thread, a thread block, and a set of simultaneously active thread blocks. For four 3D stencil computations, NVIDIA{\textquoteright}s profiling tools have been used to verify the accuracy of the quantified data traffic volumes, by trying a large number of executions with different problem sizes and thread block configurations. Moreover, by introducing an imbalance coefficient, together with the known realistic memory bandwidths, we can predict the execution time usage based on the quantified data traffic volumes. For the four 3D stencils, the average error of the time prediction is 6.9\% for a baseline implementation approach, whereas for a blocking implementation approach the average prediction error is 9.5\%.},
	keywords = {3D stencil methods, GPU, performance modeling},
	issn = {0920-8542},
	doi = {10.1007/s11227-015-1392-1},
	url = {http://link.springer.com/article/10.1007/s11227-015-1392-1},
	author = {Su, Huayou and Cai, Xing and Wen, Mei and Zhang, Chunyuan}
}
@article {Simula.simula.2246,
	title = {Towards Simulation of Subcellular Calcium Dynamics at Nanometre Resolution},
	journal = {International Journal of High Performance Computing Applications},
	volume = {29},
	year = {2015},
	pages = {51-63},
	publisher = {SAGE},
	abstract = {Numerical simulation of subcellular\&nbsp;\&nbsp;dynamics with a resolution down to one nanometre can be an important tool for discovering the physiological cause of many heart diseases. The requirement of enormous computational power, however, has made such simulations prohibitive so far. By using up to 12,288 Intel Xeon Phi 31S1P coprocessors on the new hybrid cluster Tianhe-2, which is the new number one supercomputer of the world, we have achieved 1.27 Pflop/s in double precision, which brings us much closer to the nanometre resolution. This is the result of efficiently using the hardware on different levels: (1) a single Xeon Phi (2) a single compute node that consists of a host and three coprocessors, and (3) a huge number of interconnected nodes. To overcome the challenge of programming Intel{\textquoteright}s new many-integrated core (MIC) architecture, we have adopted techniques such as vectorization, hierarchical data blocking, register data reuse, offloading computations to the coprocessors, and pipelining computations with intra-/inter-node communications.},
	doi = {10.1177/1094342013514465},
	author = {Chai, Jun and Johan Elon {Hake} and Wu, Nan and Wen, Mei and Cai, Xing and Glenn T. {Lines} and Yang, Jing and Su, Huayou and Zhang, Chunyuan and Liao, Xiangke}
}
@article {Simula.simula.2096,
	title = {High Efficient Sedimentary Basin Simulations on Hybrid CPU-GPU Clusters},
	journal = {Cluster Computing},
	volume = {17},
	number = {2},
	year = {2014},
	pages = {359-369},
	publisher = { },
	doi = {10.1007/s10586-013-0300-9},
	author = {Wen, Mei and Su, Huayou and Wei, Wenjie and Wu, Nan and Cai, Xing and Zhang, Chunyuan}
}
@article {Simula.simula.1490,
	title = {Balancing Efficiency and Accuracy for Sediment Transport Simulations},
	journal = {Computational Science \& Discovery},
	volume = {6},
	number = {1},
	year = {2013},
	pages = {015011},
	abstract = {Simulating multi-lithology sediment transport requires numerically solving a fully-coupled system of nonlinear partial differential equations. The most standard approach is to simultaneously update all the unknown fields at each time step. Such a fully-implicit strategy is computationally demanding due to the need of Newton-Raphson iterations, each having to set up and solve a large system of linearized algebraic equations. Fully-explicit numerical schemes that do not solve linear systems are possible to devise, but suffer from lower numerical stability and accuracy. If we count the total number of floating-point operations needed to achieve stable numerical solutions with a prescribed level of accuracy, the fully-implicit approach probably wins over its fully-explicit counterpart. However, the latter may nevertheless win in the overall computation time, because computers achieve higher hardware efficiency for simpler numerical computations. Adding to this competition, there are semi-implicit numerical schemes that lie between the two extremes. This paper has two novel contributions. First, we device a new semi-implicit scheme that has secondorder accuracy in the temporal direction. Second, and more importantly, we propose a simple prediction model for the overall computation time on multicore architectures, applicable to many numerical implementations. Based on performance prediction, appropriate numerical schemes can be chosen by considering accuracy, stability, and computing speed at the same time. Our methodology is tested by numerical experiments modeling the sediment transport in Monterey Bay.},
	doi = {10.1088/1749-4699/6/1/015011},
	author = {Wei, Wenjie and Clark, Stuart and Su, Huayou and Wen, Mei and Cai, Xing}
}
@inproceedings {Simula.simula.1972,
	title = {On the GPU Performance of 3D Stencil Computations Implemented in OpenCL},
	journal = {Proceedings of International Supercomputing Conference, ISC 2013},
	volume = {7905},
	year = {2013},
	pages = {125-135},
	publisher = {Springer},
	type = {Conference},
	address = {Berlin Heidelberg New York},
	keywords = {Conference},
	doi = {10.1007/978-3-642-38750-0_10},
	author = {Su, Huayou and Wu, Nan and Wen, Mei and Zhang, Chunyuan and Cai, Xing},
	editor = {Julian Martin {Kunkel} and Ludwig, Thomas and Hans Werner {Meuer}}
}
@inproceedings {Simula.simula.2100,
	title = {On the GPU-CPU Performance Portability of OpenCL for 3D Stencil Computations},
	journal = {Proceedings of IEEE 19th International Conference on Parallel and Distributed Systems},
	year = {2013},
	pages = {78-85},
	publisher = {IEEE},
	type = {Conference},
	address = {Los Alamitos, California  {\textbullet} Washington {\textbullet} Tokyo},
	keywords = {Conference},
	doi = {10.1109/ICPADS.2013.23},
	author = {Su, Huayou and Wu, Nan and Wen, Mei and Zhang, Chunyuan and Cai, Xing}
}
@inproceedings {Simula.simula.1970,
	title = {Performance of Sediment Transport Simulations on NVIDIA{\textquoteright}s Kepler Architecture},
	journal = {The International Conference on Computational Science, ICCS 2013},
	volume = {18},
	year = {2013},
	pages = {1275-1281},
	publisher = {Elsevier},
	type = {Conference},
	keywords = {Conference},
	doi = {10.1016/j.procs.2013.05.294},
	author = {Su, Huayou and Wu, Nan and Wen, Mei and Zhang, Chunyuan and Cai, Xing},
	editor = {Alexandrov, Vassil and Lees, Michael and Krzhizhanovskaya, Valeria and Dongarra, Jack and Peter M.A. {Sloot}}
}
@article {Simula.simula.1394,
	title = {Resource-Efficient Utilization of CPU/GPU-Based Heterogeneous Supercomputers for Bayesian Phylogenetic Inference},
	journal = {The Journal of Supercomputing},
	volume = {66},
	number = {1},
	year = {2013},
	pages = {364-380},
	doi = {10.1007/s11227-013-0911-1},
	author = {Chai, Jun and Su, Huayou and Wen, Mei and Cai, Xing and Wu, Nan and Zhang, Chunyuan}
}
@inproceedings {Simula.simula.1342,
	title = {Using 1000+ GPUs and 10000+ CPUs for Sedimentary Basin Simulations},
	journal = {Proceedings of IEEE Cluster 2012},
	year = {2012},
	pages = {27-35},
	publisher = {IEEE},
	type = {Conference},
	address = {Los Alamitos, California  {\textbullet} Washington {\textbullet} Tokyo},
	keywords = {Conference},
	doi = {10.1109/CLUSTER.2012.37},
	author = {Wen, Mei and Su, Huayou and Wei, Wenjie and Wu, Nan and Cai, Xing and Zhang, Chunyuan}
}
