Learn More
In this paper, we describe our experiment developing an implementation of the Linpack benchmark for TianHe-1, a petascale CPU/GPU supercomputer system, the largest GPU-accelerated system ever attempted before. An adaptive optimization framework is presented to balance the workload distribution across the GPUs and CPUs with the negligible runtime overhead,(More)
As the size of large-scale computer systems increases, their mean-time-between-failures are becoming significantly shorter than the execution time of many current scientific applications. To complete the execution of scientific applications, they must tolerate hardware failures. Conventional rollback-recovery protocols redo the computation of the crashed(More)
This paper addresses the issue of fault tolerance in parallel computing, and proposes a new method named parallel recomputing. Such method achieves fault recovery automatically by using surviving processes to recompute the workload of failed processes in parallel. The paper firstly defines the fault tolerant parallel algorithm (FTPA) as the parallel(More)
This paper proposes an optimization method of data saving for application-level checkpointing based on the live-variable analysis method for MPI programs. We presents the implementation of a source-to-source precompiler (CAC) for automating applicationlevel checkpointing based on the optimization method. The experiment shows that CAC is capable of(More)
As the size of today's high performance computers continue to grow, node failures in these computers are becoming frequent events. Although checkpoint is the typical technique to tolerate such failures, it often introduces a considerable overhead and has shown poor scalability on today's large scale systems. In this paper we defined a new term called fault(More)
Application-level checkpointing can decrease the overhead of fault tolerance by minimizing the amount of checkpoint data. However this technique requires the programmer to manually choose the critical data that should be saved. In this paper, we firstly propose a live-variable analysis method for MPI programs. Then, we provide an optimization method of data(More)
HPCG has become a new metric for the design and ranking of HPC. By incorporating a local symmetric Gauss-Seidel preconditioned, HPCG implements the Conjugate Gradient method to solve a sparse linear system. HPCG performs poorly with irregular memory access and may consume a great deal of MPI resources when it is executed on supercomputers. This paper(More)