DMTCP (Distributed MultiThreaded CheckPointing) is a transparent user-level checkpointing package for distributed applications. Checkpointing and restart is demonstrated for a wide range of over 20 well known applications, including MATLAB, Python, TightVNC, MPICH2, OpenMPI, and runCMS. RunCMS runs as a 680 MB image in memory that includes 540 dynamic libraries, and is used for the CMS experiment of the Large Hadron Collider at CERN. DMTCP transparently checkpoints general cluster computations consisting of many nodes, processes, and threads; as well as typical desktop applications. On 128 distributed cores (32 nodes), checkpoint and restart times are typically 2 seconds, with negligible run-time overhead. Typical checkpoint times are reduced to 0.2 seconds when using forked checkpointing. Experimental results show that checkpoint time remains nearly constant as the number of nodes increases on a medium-size cluster. DMTCP automatically accounts for fork, exec, ssh, mutexes semaphores, TCP IP sockets, UNIX domain sockets, pipes, ptys (pseudo-terminals), terminal modes, ownership of controlling terminals, signal handlers, open file descriptors, shared open file descriptors, I O (including the readline library), shared memory (via mmap), parent-child process relationships, pid virtualization, and other operating system artifacts. By emphasizing an unprivileged, user-space approach, compatibility is maintained across Linux kernels from 2.6.9 through the current 2.6.28. Since DMTCP is unprivileged and does not require special kernel modules or kernel patches, DMTCP can be incorporated and distributed as a checkpoint-restart module within some larger package.
Much MPI-specific work has been based on coordinated checkpointing and the use of hooks into communication by the MPI library @cite_10 @cite_6 . In contrast, our goal is to support more general distributed scientific software.
@cite_10 @cite_6
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The running times of many computational science applications, such as protein-folding using ab initio methods, are much longer than the mean-time-to-failure of high-performance computing platforms. To run to completion, therefore, these applications must tolerate hardware failures.In this paper, we focus on the stopping failure model in which a faulty process hangs and stops responding to the rest of the system. We argue that tolerating such faults is best done by an approach called application-level coordinated non-blocking checkpointing, and that existing fault-tolerance protocols in the literature are not suitable for implementing this approach.We then present a suitable protocol, which is implemented by a co-ordination layer that sits between the application program and the MPI library. We show how this protocol can be used with a precompiler that instruments C MPI programs to save application and MPI library state. An advantage of our approach is that it is independent of the MPI implementation. We present experimental results that argue that the overhead of using our system can be small. Most high-performance, scientific libraries have adopted hybrid parallelization schemes - such as the popular MPI+OpenMP hybridization - to benefit from the capacities of modern distributed-memory machines. While these approaches have shown to achieve high performance, they require a lot of effort to design and maintain sophisticated synchronization communication strategies. On the other hand, task-based programming paradigms aim at delegating this burden to a runtime system for maximizing productivity. In this article, we assess the potential of task-based fast multipole methods (FMM) on clusters of multicore processors. We propose both a hybrid MPI+task FMM parallelization and a pure task-based parallelization where the MPI communications are implicitly handled by the runtime system. The latter approach yields a very compact code following a sequential task-based programming model. We show that task-based approaches can compete with a hybrid MPI+OpenMP highly optimized code and that furthermore the compact task-based scheme fully matches the performance of the sophisticated, hybrid MPI+task version, ensuring performance while maximizing productivity. We illustrate our discussion with the ScalFMM FMM library and the StarPU runtime system. As computational clusters increase in size, their mean time to failure reduces drastically. Typically, checkpointing is used to minimize the loss of computation. Most checkpointing techniques, however, require central storage for storing checkpoints. This results in a bottleneck and severely limits the scalability of checkpointing, while also proving to be too expensive for dedicated checkpointing networks and storage systems. We propose a scalable replication-based MPI checkpointing facility. Our reference implementation is based on LAM MPI; however, it is directly applicable to any MPI implementation. We extend the existing state of fault-tolerant MPI with asynchronous replication, eliminating the need for central or network storage. We evaluate centralized storage, a Sun-X4500-based solution, an EMC storage area network (SAN), and the Ibrix commercial parallel file system and show that they are not scalable, particularly after 64 CPUs. We demonstrate the low overhead of our checkpointing and replication scheme with the NAS Parallel Benchmarks and the High-Performance LINPACK benchmark with tests up to 256 nodes while demonstrating that checkpointing and replication can be achieved with a much lower overhead than that provided by current techniques. Finally, we show that the monetary cost of our solution is as low as 25 percent of that of a typical SAN parallel-file-system-equipped storage system. Global Computing platforms, large scale clusters and future TeraGRID systems gather thousands of nodes for computing parallel scientific applications. At this scale, node failures or disconnections are frequent events. This Volatility reduces the MTBF of the whole system in the range of hours or minutes. We present MPICH-V, an automatic Volatility tolerant MPI environment based on uncoordinated checkpoint roll-back and distributed message logging. MPICH-V architecture relies on Channel Memories, Checkpoint servers and theoretically proven protocols to execute existing or new, SPMD and Master-Worker MPI applications on volatile nodes. To evaluate its capabilities, we run MPICH-V within a framework for which the number of nodes, Channels Memories and Checkpoint Servers can be completely configured as well as the node Volatility. We present a detailed performance evaluation of every component of MPICH-V and its global performance for non-trivial parallel applications. Experimental results demonstrate good scalability and high tolerance to node volatility.