Understanding metadata latency with MDWorkbench

While parallel file systems often satisfy the need of applica- tions with bulk synchronous I/O, they lack capabilities of dealing with metadata intense workloads. Typically, in procurements, the focus lies on the aggregated metadata throughput using the MDTest benchmark. However, metadata performance is crucial for interactive use. Metadata benchmarks involve even more parameters compared to I/O benchmarks. There are several aspects that are currently uncovered and, therefore, not in the focus of vendors to investigate. Particularly, response latency and interactive workloads operating on a working set of data. The lack of ca- pabilities from file systems can be observed when looking at the IO-500 list, where metadata performance between best and worst system does not differ significantly. In this paper, we introduce a new benchmark called MDWorkbench which generates a reproducible workload emulating many concurrent users or – in an alternative view – queuing systems. This benchmark pro- vides a detailed latency profile, overcomes caching issues, and provides a method to assess the quality of the observed throughput. We evaluate the benchmark on state-of-the-art parallel file systems with GPFS (IBM Spectrum Scale), Lustre, Cray’s Datawarp, and DDN IME, and conclude that we can reveal characteristics that could not be identified before

Towards decoupling the selection of compression algorithms from quality constraints – an investigation of lossy compression efficiency

Data intense scientific domains use data compression to reduce the storage space needed. Lossless data compression preserves information accurately but lossy data compression can achieve much higher compression rates depending on the tolerable error margins. There are many ways of defining precision and to exploit this knowledge, therefore, the field of lossy compression is subject to active research. From the perspective of a scientist, the qualitative definition about the implied loss of data precision should only matter. With the Scientific Compression Library (SCIL), we are developing a meta-compressor that allows users to define various quantities for acceptable error and expected performance behavior. The library then picks a suitable chain of algorithms yielding the user’s requirements, the ongoing work is a preliminary stage for the design of an adaptive selector. This approach is a crucial step towards a scientifically safe use of much-needed lossy data compression, because it disentangles the tasks of determining scientific characteristics of tolerable noise, from the task of determining an optimal compression strategy. Future algorithms can be used without changing application code. In this paper, we evaluate various lossy compression algorithms for compressing different scientific datasets (Isabel, ECHAM6), and focus on the analysis of synthetically created data that serves as blueprint for many observed datasets. We also briefly describe the available quantitiesof SCIL to define data precision and introduce two efficient compression algorithms for individualdata points. This shows that the best algorithm depends on user settings and data properties

Towards Automatic Load Balancing of a Parallel File System with Subfile Based Migration

Nowadays scientific applications address complex problems in nature. As a consequence they have a high demand for computation power and for an I/O infrastructure providing performant access to data. These demands are satisfied by various supercomputers in order to engage grand challenges. From the operator's point of view it is important to keep the available resources of such a multi dollar machine busy, while the end-user is concerned about the runtime of the application. Evidently it is vital to avoid idle times in an application and congestion of network as well as of the I/O subsystem to ensure maximum concurrency and thus efficiency. Load balancing is a technique which tackles this issues. While load balancing has been evaluated in detail for computational parts of a program, analysis of load imbalance for complex storage environments in High Performance Computing has to be addressed as well. Often parallel file systems like Lustre, GPFS, or PVFS2 are deployed to meet the needs of a fast I/O infrastructure. This thesis evaluates the impact of unbalanced workloads in such parallel file systems exemplarily on PVFS2 and extends the environment to allow dynamic (and adaptive) load balancing. Some cases leading to unbalanced workloads are discussed, namely unbalanced access patterns, inhomogeneous hardware, and rebuilds after crashes in an environment promising high availability. Important factors related to the performance are described, this allows to build simple performance models on which the impact of such load imbalances can be assessed. Some potential countermeasures to fix these unbalanced workloads are discussed in the thesis. While most cases could be alleviated by static load balancing mechanisms a dynamic load balancing seems important to make up for environments with fluctuating performance characteristics. In the thesis extensions to the software environment are designed and realized that provide capabilities to detect bottlenecks and to fix them by moving data from higher loaded servers to lower loaded servers. Therefore, further mechanisms are integrated into PVFS2, which allow and support dynamic decisions to move data by a load-balancer. A first heuristics is implemented using the extensions to demonstrate how they can be used to build a dynamic load-balancer. Experiments are run with balanced as well as unbalanced workloads to show the server behavior.Also a few experiments with the developed load-balancer in a real environment are made. These results demonstrate problematic issues and demonstrate that load balancing techniques could be successfully applied to increase productivity

Exascale storage systems: an analytical study of expenses

The computational power and storage capability of supercomputers are growing at a different pace, with storage lagging behind; the widening gap necessitates new approaches to keep the investment and running costs for storage systems at bay. In this paper, we aim to unify previous models and compare different approaches for solving these problems. By extrapolating the characteristics of the German Climate Computing Center's previous supercomputers to the future, cost factors are identified and quantified in order to foster adequate research and development. Using models to estimate the execution costs of two prototypical use cases, we are discussing the potential of three concepts: re-computation, data deduplication and data compression

Performance Analysis of the PVFS2 Persistency Layer

Performance of complex software with a layered architecture is strongly limited by the capabilities of each single layer. An analysis of a single layer can be done by implementing a simple stub which has a well known complexity for the functions provided by the layer. Furthermore, the performance of the remaining layers and even the whole software architecture can be measured with an efficient stub to find bottlenecks. As the Parallel Virtual Filesystem Version 2 (PVFS2) has such a layered architecture, an examination of the different layers can help to improve performance. The goal of this thesis is the analysis of the PVFS2 persistency layer. In addition, concepts for further improvement of the layers are derived from this analysis

Understanding I/O Behavior in Scientific and Data-Intensive Computing (Dagstuhl Seminar 21332)

Two key changes are driving an immediate need for deeper understanding of I/O workloads in high-performance computing (HPC): applications are evolving beyond the traditional bulk-synchronous models to include integrated multistep workflows, in situ analysis, artificial intelligence, and data analytics methods; and storage systems designs are evolving beyond a two-tiered file system and archive model to complex hierarchies containing temporary, fast tiers of storage close to compute resources with markedly different performance properties. Both of these changes represent a significant departure from the decades-long status quo and require investigation from storage researchers and practitioners to understand their impacts on overall I/O performance. Without an in-depth understanding of I/O workload behavior, storage system designers, I/O middleware developers, facility operators, and application developers will not know how best to design or utilize the additional tiers for optimal performance of a given I/O workload. The goal of this Dagstuhl Seminar was to bring together experts in I/O performance analysis and storage system architecture to collectively evaluate how our community is capturing and analyzing I/O workloads on HPC systems, identify any gaps in our methodologies, and determine how to develop a better in-depth understanding of their impact on HPC systems. Our discussions were lively and resulted in identifying critical needs for research in the area of understanding I/O behavior. We document those discussions in this report

Towards Automatic Load Balancing of a Parallel File

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