Distributed Checkpointing Mechanism for a Parallel File System

Parallel scientific applications store and retrieve very large, structured datasets. Directly supporting these structured accesses is an important step in providing high-performance solutions for these applications. High-level interfaces such as HDF5 and Parallel netCDF provide convenient APIs for accessing structured datasets, and the MPI-IO interface also supports efficient access to structured data. However, parallel file systems do not traditionally support such access. In this work we present an implementation of structured data access support in the context of the parallel virtual file system (PVFS). We call this support datatype I/O because of its similarity to MPI datatypes. This support is built by using a reusable datatype-processing component from the MPICH2 MPI implementation. We describe how this component is leveraged to efficiently process structured data representations resulting from MPI-IO operations. We quantitatively assess the solution using three test applications. We also point to further optimizations in the processing path that could be leveraged for even more efficient operation.

This paper presents a new parallel storage management approach which supports the allocation of shared storage bandwidth on a per-application basis. Existing parallel storage systems are unable to differentiate I/Os from different applications and meet per-application bandwidth requirement. This limitation presents a hurdle for applications to achieve their desired performance, which will become even more challenging as high-performance computing (HPC) systems continue to scale up with respect to both the amount of available resources and the number of concurrent applications. This paper proposes a novel solution to address this challenge through the virtualization of parallel file systems (PFSes). Such PFS virtualization is achieved with user-level PFS proxies, which interpose between native PFS clients and servers and schedule the I/Os from different applications according to the resource sharing algorithm (e.g., SFQ(D)). In this way, virtual PFSes can be created on a perapplication basis, each with a specific bandwidth share allocated according to its I/O requirement. This approach is applicable to different PFS-based parallel storage systems and can be transparently integrated with existing as well as future HPC systems. A prototype of this approach is implemented upon PVFS2, a widely used PFS, and evaluated with experiments using a typical parallel I/O benchmark (IOR). Results show that this approach's overhead is very small and it achieves effective proportional sharing under different usage scenarios.

The ever-increasing gap in performance between CPU/memory technologies and the I/O subsystem (disks, I/O buses) in modern workstations has exacerbated the I/O bottlenecks inherent in applications that access large disk resident data sets. A common technique to alleviate the I/O bottlenecks on clusters of workstations, is the use of parallel file systems. One such parallel file system is the parallel virtual file system (PVFS), which is a freely available tool to achieve high-performance I/O on Linux-based clusters. Here, we describe the performance and scalability of the UNIX I/O interface to PVFS. To illustrate the performance, we present experimental results using Bonnie++, a commonly used file system benchmark to test file system throughput; a synthetic parallel I/O application for calculating aggregate read and write bandwidths; and a synthetic benchmark which calculates the time taken to untar the Linux kernel source tree to measure performance of a large number of small file operations. We obtained aggregate read and write bandwidths as high as 550 MB/s with a Myrinet-based network and 160MB/s with fast Ethernet.

Parallel file systems (PFS) are used to distribute data processing and establish shared access to large-scale data. Despite being able to provide high I/O bandwidth on each node, PFS has difficulty utilizing the I/O bandwidth due to a single connection between the client and server nodes. To mitigate the performance bottleneck, users increase the number of connections between the nodes by modifying PFS or applications. However, it is difficult to modify PFS itself due to its complicated internal structure. Thus, PFS users manually increase the number of connections between the nodes by employing several methods. In this paper, we propose a user-transparent I/O subsystem, MulConn, to make users exploit high I/O bandwidth between nodes. To avoid the modifications of PFS and user applications, we have developed a horizontal mount procedure and two I/O scheduling policies, TtoS and TtoM, in the virtual file system (VFS) layer. We expose a single mount point that has multiple connections by modifying the mount path of VFS from vertical hierarchy to horizontal hierarchy. We also introduce two I/O scheduling policies to distribute I/O requests evenly to multiple connections. The experimental results show that MulConn improves write and read performance by up to 2.6x and 2.8x, respectively, compared with those of PFS using the existing kernel. In addition, we provide the best I/O performance that PFS can provide in the given experimental environments.

Parallel file systems (PFSes) play an essential role in high performance computing. To ensure the integrity, many PFSes are designed with a checker component, which serves as the last line of defense to bring a corrupted PFS back to a healthy state. Motivated by real-world incidents of PFS corruptions, we perform a fine-grained study on the capability of PFS checkers in this paper. We apply type-aware fault injection to specific PFS structures, and examine the detection and repair policies of PFS checkers meticulously via a well-defined taxonomy. The study results on two representative PFS checkers show that they are able to handle a wide range of corruptions on important data structures. On the other hand, neither of them is perfect: there are multiple cases where the checkers may behave sub-optimally, leading to kernel panics, wrong repairs, etc. Our work has led to a new patch on Lustre. We hope to develop our methodology into a generic framework for analyzing the checkers of diverse PFSes, and enable more elegant designs of PFS checkers for reliable high-performance computing.

Recent HPC systems utilize parallel file systems such as GPFS and Lustre to cope with the huge demand of data-intensive applications. Although most of the HPC systems provide performance tuning tools on compute nodes, there is not enough chance to tune I/O activities on parallel file systems including high-speed interconnects among compute nodes and file systems. We propose an I/O performance optimization framework using log data of parallel file systems and interconnects in a holistic way for improving performance of HPC systems including I/O nodes and parallel file systems. We demonstrate our framework at the K computer with two I/O benchmarks for the original and the enhanced MPI-IO implementations. Its I/O analysis has revealed that I/O performance improvements achieved by the enhanced MPI-IO implementation are due to effective utilization of parallel file systems and interconnects among I/O nodes compared with the original MPI-IO implementation.

As parallel file systems span larger and larger numbers of nodes in order to provide the performance and scalability necessary for modern cluster applications, the need for fault-tolerance and high data availability file systems has arisen. Modern parallel file systems spanning tens, hundreds, or even thousands of servers will require fault tolerance to avoid job failure and catastrophic data loss due to a single disk failure or server loss. Effective fault tolerance in parallel file systems must provide a high degree of data resiliency, consistency, and scalable performance. In this paper, we describe a data replication technique that meets the resiliency and consistency requirements of parallel file systems and provides scalable performance. We measure the performance of our proposed mechanism by implementing it in a popular

With the advent of new computing paradigms, parallel file systems serve not only traditional scientific computing applications but also non-scientific computing applications, such as financial computing, business, and public administration. Parallel file systems provide storage services for multiple applications. As a result, various requirements need to be met. However, parallel file systems usually provide a unified storage solution, which cannot meet specific application needs. In this paper, an extended file handle scheme is proposed to deal with this problem. The original file handle is extended to record I/O optimization information, which allows file systems to specify optimizations for a file or directory based on workload characteristics. Therefore, fine-grained management of I/O optimizations can be achieved. On the basis of the extended file handle scheme, data prefetching and small file optimization mechanisms are proposed for parallel file systems. The experimental results show that the proposed approach improves the aggregate throughput of the overall system by up to 189.75%.

Parallel file systems are important infrastructures for both cloud and high performance computing. The performance of metadata operations is critical to achieve high scalability in parallel file systems. Nevertheless, traditional parallel file systems are lack of scalable metadata service. To alleviate these problems, some previous research distributes metadata to separated large-scale clusters and uses write-optimized techniques like log-structured merge tree (LSM-tree) to store metadata. However, LSM-tree design does not consider the features of solid state drive devices (SSD) which are widely deployed in modern parallel computing systems. The design of using LSM-trees to store metadata has not explored the potential benefits of SSD devices. In this paper, we present StageFS, which is a parallel file system optimized for SSD based clusters. StageFS stores both the metadata and small files in LSM-trees for fast indexing. For larger files, the file blocks are separately stored to reduce the write amplifications. In addition, the parallel I/O feature of SSD devices is used to improve the performance of accessing directories and large files. To avoid frequent small writes, StageFS uses buffering to better utilize the bandwidth of SSD devices. Experimental results show that StageFS provides better performance in metadata operations (up to 21.28x) and small file access (1.92x to two orders of magnitude) compared with Ceph and HDFS.

High-performance computing (HPC) systems are growing more powerful by utilizing more hardware components. As the system mean-time-before-failure correspondingly drops, applications must checkpoint more frequently to make progress. However, as the system memory sizes grow faster than the bandwidth to the parallel file system, the cost of checkpointing begins to dominate application run times. Multi-level checkpointing potentially solves this problem through multiple types of checkpoints with different costs and different levels of resiliency in a single run. This solution employs lightweight checkpoints to handle the most common failure modes and relies on more expensive checkpoints for less common, but more severe failures. This theoretically promising approach has not been fully evaluated in a large- scale, production system context. We have designed the Scalable Checkpoint/Restart (SCR) library, a multi-level checkpoint system that writes checkpoints to RAM, Flash, or disk on the compute nodes in addition to the parallel file system. We present the performance and reliability properties of SCR as well as a probabilistic Markov model that predicts its performance on current and future systems. We show that multi-level checkpointing improves efficiency on existing large-scale systems and that this benefit increases as the system size grows. In particular, we developed low-cost checkpoint schemes that are 100x-1000x faster than the parallel file system and effective against 85% of our system failures. This leads to a gain in machine efficiency of up to 35%, and it reduces the the load on the parallel file system by a factor of two on current and future systems.

High-performance computing (HPC) systems are growing more powerful by utilizing more components. As the system mean time before failure correspondingly drops, applications must checkpoint frequently to make progress. However, at scale, the cost of checkpointing becomes prohibitive. A solution to this problem is multilevel checkpointing, which employs multiple types of checkpoints in a single run. Lightweight checkpoints can handle the most common failure modes, while more expensive checkpoints can handle severe failures. We designed a multilevel checkpointing library, the Scalable Checkpoint/Restart (SCR) library, that writes lightweight checkpoints to node-local storage in addition to the parallel file system. We present probabilistic Markov models of SC’s performance. We show that on future large-scale systems, SCR can lead to a gain in machine efficiency of up to 35%, and reduce the load on the parallel file system by a factor of two. Additionally, we predict that checkpoint scavenging, or only writing checkpoints to the parallel file system on application termination, can reduce the load on the parallel file system by 20× on today’s systems and still maintain high application efficiency.

Scalable I/O aggregation for asynchronous multi-level checkpointing

SideIO: A Side I/O system framework for hybrid scientific workflow

As storage systems evolve, the block-based design of today's disks is becoming inadequate. As an alternative, object-based storage devices (OSDs) offer a view where the disk manages data layout and keeps track of various attributes about data objects. By moving functionality that is traditionally the responsibility of the host OS to the disk, it is possible to improve overall performance and simplify management of a storage system. The capabilities of OSDs will also permit performance improvements in parallel file systems, such as further decoupling metadata operations and thus reducing metadata server bottlenecks.

Beowulf workstations are a new approach to parallel computing that combine the characteristics of the Pile-Of-PC architecture with freely available software and provide high performance at a low cost for many applications. In order to provide this performance in the face of increasing problem sizes, efficient out of core (OOC) methods must be investigated. In light of shortcomings in operating system virtual memory performance, we have implemented a parallel file system and a new interface tailored to OOC algorithms to provide efficient and convenient access to disk resources in Beowulf workstations. This paper focuses on how these components extend the range of problem sizes which can be efficiently solved on Beowulf workstations in the context of a sample application, a Gauss-Seidel iterative solver. Execution times of the solver when using traditional virtual memory and the parallel file system are compared to characterize performance for various problem sizes, and it is concluded that the combination of parallel file system and new interface significantly increases the size of problems that can be efficiently solved.