Abstract: Halo-based galaxy group finder (HGGF) is an effective algorithm that accomplishes the task of galaxy group finding based on galaxy coordinates,redshift and mass etc.,and provides great help in the research of galaxy group formation and evolution.However,current pure OpenMP implementation of the algorithm is limited by the resource of the underlying single compute node when dealing with large-scale group finding problems.One of the possible solutions is using resources from multiple nodes to reduce execution time while solving large-size galaxy group finding problem.Therefore,it is essential to redesign and implement the algorithm.The major hurdle for such an attempt is remoting memory access due to semi-random galaxy access in the algorithm which damages the performance in multi-node environment.To tackle such a problem,we paralleled the algorithm with adjacent galaxy list design and used unified parallel C (UPC) to implement it.2.25,2.78 and 5.07 times speedup for the kernel were achieved with 4,8 and 16 nodes respectively.Meanwhile,the memory requirement on each node was also reduced significantly.Experiments of OpenMP version of the algorithm on SGI UV 2000 show that due to the nature of the program and the features of NUMA architecture,programs with random memory access behavior like HGGF may not readily benefit from the large number of threads and shared memory provided by such machines.Two-level parallel design that takes advantage of locality principle on distributed memory clusters may be a better solution.

Key words: High performance computing,Galaxy group finding,Parallel computing,UPC,OpenMP