MilkyWay-2 supercomputer： system and application

On June 17, 2013, MilkyWay-2 （Tianhe-2） supercomputer was crowned as the fastest supercomputer in the world on the 41th TOP500 list. This paper provides an overview of the MilkyWay-2 project and describes the design of hardware and software systems. The key architecture features of MilkyWay-2 are highlighted, including neo-heterogeneous compute nodes integrating commodity- off-the-shelf processors and accelerators that share similar instruction set architecture, powerful networks that employ proprietary interconnection chips to support the massively parallel message-passing communications, proprietary 16- core processor designed for scientific computing, efficient software stacks that provide high performance file system, emerging programming model for heterogeneous systems, and intelligent system administration. We perform extensive evaluation with wide-ranging applications from LINPACK and Graph500 benchmarks to massively parallel software deployed in the system.

Fast Parallel Cutoff Pair Interactions for Molecular Dynamics on Heterogeneous Systems

Heterogeneous systems with both Central Processing Units （CPUs） and Graphics Processing Units （GPUs） are frequently used to accelerate short-ranged Molecular Dynamics （MD） simulations. The most time-consuming task in short-ranged MD simulations is the computation of particle-to-particle interac- tions. Beyond a certain distance, these interactions decrease to zero. To minimize the operations to investi- gate distance, previous works have tiled interactions by employing the spatial attribute, which increases the memory access and GPU computations, hence decreasing performance. Other studies ignore the spatial attribute and construct an all-versus-all interaction matrix, which has poor scalability. This paper presents an improved algorithm. The algorithm first bins particles into voxels according to the spatial attributes, and then tiles the all-versus-all matrix into voxel-versus-voxel sub-matrixes. Only the sub-matrixes between neighbor- ing voxels are computed on the GPU. Therefore, the algorithm reduces the distance examine operations and limits additional memory access and GPU computations. This paper also adopts a multi-level program- ming model to implement the algorithm on multi-nodes of Tianhe-lA. By employing （1） a patch design to ex- ploit parallelism across the simulation domain, （2） a communication overlapping method to overlap the communications between CPUs and GPUs, and （3） a dynamic workload balancing method to adjust the workloads among compute nodes, the implementation achieves a speedup of 4.16x on one NVIDIA Tesla M2050 GPU compared to a 2.93 GHz six-core Intel Xeon X5670 CPU. In addition, it runs 2.41x faster on 256 compute nodes of Tianhe-lA （with two CPUs and one GPU inside a node） than on 256 GPU-excluded nodes.