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 process since the last checkpoint on a single processor. As a result, the recovery time of all protocols is no less than the time between the last checkpoint and the crash. In this paper, we propose a new application-level fault-tolerant approach for parallel applications called the fault-tolerant parallel algorithm (FTPA), which provides fast self-recovery. When fail-stop failures occur and are detected, all surviving processes recompute the workload of failed processes in parallel. FTPA, however, requires the user to be involved in fault tolerance. In order to ease the FTPA implementation, we developed get it fault-tolerant (GiFT), a source-to-source precompiler tool to automate the FTPA implementation. We evaluate the performance of FTPA with parallel matrix multiplication and five kernels of NAS Parallel Benchmarks on a cluster system with 1,024 CPUs. The experimental results show that the performance of FTPA is better than the performance of the traditional checkpointing approach.

In high performance parallel computing, energy optimization for parallel loops becomes one key because the time of loops often takes a significant part of the whole execution time. Energy-constrained problem is one of the important research focuses. This paper studies energy-constrained problem based on OpenMP static loop scheduling. Firstly, we propose energy-constrained static scheduling algorithm (ECSS), which utilizes DVS to scale down voltage/frequency of the light-loaded processors in terms of energy constraint. Secondly, we propose Energy Constraint based Performance-Optimal Static Scheduling algorithm (ECPOSS), which combines loop rescheduling and DVS for the better performance under the same energy constraint. We prove ECPOSS can obtain the best performance under the same energy constraint. Through testing NPB3.2-OMP programs on 20-160 multiprocessor simulation environment, we evaluate the effectiveness of our algorithms. Experimental results show the performance of ECPOSS is better than that of ECSS by 4.81% under the 50% energy constraint on 100 processors.

Application-level checkpointing is a promising technology in the domain of large-scale scientific computing. The consistency of global checkpoint must be carefully guaranteed in order to correctly restore the computation. Usually, some complex coordinated protocols are employed to ensure the consistency of global checkpoint, which require logging orphan or in-transit messages during checkpointing. These protocols complicate the recovery of the computation and increase the checkpoint overhead due to logging message. In this paper, a new method which ensures the consistency of global checkpoint by static analysis is proposed. The method identifies the safe checkpointing regions in MPI programs, where the global checkpoint is always strongly consistent. All checkpoints are located in those safe checkpoint regions. During checkpointing, the method will not log any messages and introduce no extra overhead. The method was implemented and integrated into ALEC, which is a source-to-source precompiler for automating application-level checkpointing. The experimental results show that our method is effective.

One of the basic problems related to the efficient application-level checkpointing is the placement of checkpoints in the source codes. In this paper we discuss two common questions with a source-to-source precompiler ALEC: 1) if there are N checkpoints in the application's source code, how to pick M checkpoints out of them minimizing the total amount of checkpoint data? 2) if there are no checkpoint in the application's source code, how to insert a set of checkpoints minimizing the amount of checkpoint data? We reveal that these two questions can both be abstracted as a mathematic model which is similar to the 0-1 integer programming model, and the model can be solved using implicit enumeration method. The solving methods proposed in the paper have been implemented and integrated into ALEC. Experimental results show that the method is efficient.

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 saving for application-level checkpointing based on the analysis method. Based on the theoretical foundation, we implement a source-to-source precompiler (ALEC) to automate application-level checkpointing. Finally, we evaluate the performance of five FORTRAN/MPI programs which are transformed and integrated checkpointing features by ALEC on a 512-CPU cluster system. The experimental results show that i) the application-level checkpointing based on live-variable analysis for MPI programs can efficiently reduce the amount of checkpoint data, thereby decrease the overhead of checkpoint and restart; ii) ALEC is capable of automating application-level checkpointing correctly and effectively.

Fault tolerance is a critical issue in the arena of large-scale computing. The fault-tolerant parallel algorithm (FTPA) is an application-level technique for tolerating hardware failures. FTPA achieves fast failure recovery making use of parallel recomputing. However, it complicates the coding of the application program. This paper uses compiler technology to automate the design of FTPA, and introduces the implementation of a tool called GiFT (Get it Fault-Tolerant). GiFT utilizes the extended data-flow analysis to choose the state needed by failure recovery, exploits the parallel recomputing time model to compute the optimal number of recomputing processes, and uses parallelization technologies to generate parallel recomputing codes. The experimental results show that original MPI programs can be transformed into the FTPA counterparts by GiFT correctly, and the performance of GiFT-generated FTPA programs is comparable to the performance of hand-modified FTPA programs.

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 automating application-level checkpointing correctly and reducing checkpoint data effectively.