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.
DejaVu @cite_2 (whose development overlapped that of DMTCP) also provides transparent user-level checkpointing of distributed process based on sockets. However, DejaVu appears to be much slower than DMTCP. For example, in the Chombo benchmark, Ruscio et al report executing ten checkpoints per hour with 45 checkpoints in 2 seconds, with essentially zero overhead between checkpoints. Nevertheless, DejaVu is also able to checkpoint InfiniBand connections by using a customized version of MVAPICH. DejaVu takes a more invasive approach than DMTCP, by logging all communication and by using page protection to detect modification of memory pages between checkpoints. This accounts for additional overhead during normal program execution that is not present in DMTCP. Since DejaVu was not publicly available at the time of this writing, a direct timing comparison on a common benchmark was not possible.
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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. Multiple threads running in a single, shared address space is a simple model for writing parallel programs for symmetric multiprocessor (SMP) machines and for overlapping I O and computation in programs run on either SMP or single processor machines. Often a long running program’s user would like the program to save its state periodically in a checkpoint from which it can recover in case of a failure. This paper introduces the first system to provide checkpointing support for multithreaded programs that use LinuxThreads, the POSIX based threads library for Linux. The checkpointing library is simple to use, automatically takes checkpoint, is flexible, and efficient. Virtually all of the overhead of the checkpointing system comes from saving the checkpoint to disk. The checkpointing library added no measurable overhead to tested application programs when they took no checkpoints. Checkpoint file size is approximately the same size as the checkpointed process’s address space. On the current implementation WATER-SPATIAL from the SPLASH2 benchmark suite saved a 2.8 MB checkpoint in about 0.18 seconds for local disk or about 21.55 seconds for an NFS mounted disk. The overhead of saving state to disk can be minimized through various techniques including varying the checkpoint interval and excluding regions of the address space from checkpoints. 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. Next-generation non-volatile memories (NVMs) promise DRAM-like performance, persistence, and high density. They can attach directly to processors to form non-volatile main memory (NVMM) and offer the opportunity to build very low-latency storage systems. These high-performance storage systems would be especially useful in large-scale data center environments where reliability and availability are critical. However, providing reliability and availability to NVMM is challenging, since the latency of data replication can overwhelm the low latency that NVMM should provide. We propose Mojim, a system that provides the reliability and availability that large-scale storage systems require, while preserving the performance of NVMM. Mojim achieves these goals by using a two-tier architecture in which the primary tier contains a mirrored pair of nodes and the secondary tier contains one or more secondary backup nodes with weakly consistent copies of data. Mojim uses highly-optimized replication protocols, software, and networking stacks to minimize replication costs and expose as much of NVMM?s performance as possible. We evaluate Mojim using raw DRAM as a proxy for NVMM and using an industrial NVMM emulation system. We find that Mojim provides replicated NVMM with similar or even better performance than un-replicated NVMM (reducing latency by 27 to 63 and delivering between 0.4 to 2.7X the throughput). We demonstrate that replacing MongoDB's built-in replication system with Mojim improves MongoDB's performance by 3.4 to 4X.