Long-running MPI applications on clusters and grids that are prone to node and network failures, motivates the use of fault tolerant MPI implementations. However, previous fault tolerant MPIs lack the ability to allow the user to easily choose appropriate fault recovery strategies according to the execution environment, independent of the application codes-rather, the user often had to hard-code restoration strateties in accordance to diverse sets of fault patterns, which could be numerous: for instance, if the fault is transient to a particular process, we merely have to restart the process on the same computing node; on the other hand, if the fault is due to repetitive hardware unreliability, we must migrate the process to a new node in its recovery. ABARIS is our new Fault/Recovery model aware component framework for MPI, where users can customize MPI fault detection and recovery algorithms according to their application and execution environmental requirements by merely selecting appropriate fault/recovery components, independent of the application code. Currently, the ARA-BIS framework prototype is implemented on top of MPICH-P4MPD. Preliminary evaluation of the prototype using NPB on our MPI fault simulator demonstrates that overhead compared to the original MPICH-P4MPD is almost negligible (less than 1%) under normal execution, and when faults occur, appropriate selections and pairings of fault model and recovery method components for corresponding to the execution environment is significant to the overall execution time.

No abstract

No abstract

In a computer cluster composed of many nodes, the mean time between failures becomes shorter as the number of nodes increases. This may mean that lengthy tasks cannot be performed, because they will be interrupted by failure. Therefore, fault tolerance has become an essential part of high-performance computing. Partial message logging forms clusters of processes, and coordinates a series of checkpoints to log messages between groups. Our study proposes a system of two features to improve the efficiency of partial message logging: 1) the communication log used in the clustering is recorded at runtime, and 2) a graph partitioning algorithm reduces the complexity of the system by geometrically partitioning a grid graph. The proposed system is evaluated by executing a scientific application. The results of process clustering are compared to existing methods in terms of the clustering performance and quality.

No abstract

As we move towards higher-density, larger-scale, and lowerpower computing hardware, new types of failures are being experienced with increasing frequency. Hardware designed for the post-Moore generation are also bringing about novel resiliency challenges. In order to improve the efficiency of resiliency methods, fault injection plays an important role in understanding how errors affect the OS and application. Memory-state-aware fault injection, in particular, can be used to investigate the memory-related faults caused by using current and future hardware under extreme conditions and assess the costs/benefit trade-off of resiliency methods. We introduce MH-QEMU, a memorystate-aware fault injection platform implemented by extending a virtual machine (VM) to intercepting memory accesses. MH-QEMU supports collecting the physical and virtual addresses of memory accesses and defining appropriate injections condition using the collected information. MH-QEMU incurs a 3.4× overhead, and we demonstrate how row-hammer faults can be injected using MH-QEMU to analyzing the resiliency modified NPB CG's algorithm.

No abstract