Analyzing data properties using statistical sampling techniques – illustrated on scientific file formats and compression features

Understanding the characteristics of data stored in data centers helps computer scientists in identifying the most suitable storage infrastructure to deal with these workloads. For example, knowing the relevance of file formats allows optimizing the relevant formats but also helps in a procurement to define benchmarks that cover these formats. Existing studies that investigate performance improvements and techniques for data reduction such as deduplication and compression operate on a small set of data. Some of those studies claim the selected data is representative and scale their result to the scale of the data center. One hurdle of running novel schemes on the complete data is the vast amount of data stored and, thus, the resources required to analyze the complete data set. Even if this would be feasible, the costs for running many of those experiments must be justified. This paper investigates stochastic sampling methods to compute and analyze quantities of interest on file numbers but also on the occupied storage space. It will be demonstrated that on our production system, scanning 1 % of files and data volume is sufficient to deduct conclusions. This speeds up the analysis process and reduces costs of such studies significantly. The contributions of this paper are: (1) the systematic investigation of the inherent analysis error when operating only on a subset of data, (2) the demonstration of methods that help future studies to mitigate this error, (3) the illustration of the approach on a study for scientific file types and compression for a data center

Analyzing data properties using statistical sampling: illustrated on scientific file formats

Understanding the characteristics of data stored in data centers helps computer scientists in identifying the most suitable storage infrastructure to deal with these workloads. For example, knowing the relevance of file formats allows optimizing the relevant formats but also helps in a procurement to define benchmarks that cover these formats. Existing studies that investigate performance improvements and techniques for data reduction such as deduplication and compression operate on a subset of data. Some of those studies claim the selected data is representative and scale their result to the scale of the data center. One hurdle of running novel schemes on the complete data is the vast amount of data stored and, thus, the resources required to analyze the complete data set. Even if this would be feasible, the costs for running many of those experiments must be justified. This paper investigates stochastic sampling methods to compute and analyze quantities of interest on file numbers but also on the occupied storage space. It will be demonstrated that on our production system, scanning 1% of files and data volume is sufficient to deduct conclusions. This speeds up the analysis process and reduces costs of such studies significantly

Scalable parallelization of stencils using MODA

The natural and the design limitations of the evolution of processors, e.g., frequency scaling and memory bandwidth bottlenecks, push towards scaling applications on multiple-node configurations besides to exploiting the power of each single node. This introduced new challenges to porting applications to the new infrastructure, especially with the heterogeneous environments. Domain decomposition and handling the resulting necessary communication is not a trivial task. Parallelizing code automatically cannot be decided by tools in general as a result of the semantics of the general-purpose languages. To allow scientists to avoid such problems, we introduce the Memory-Oblivious Data Access (MODA) technique, and use it to scale code to configurations ranging from a single node to multiple nodes, supporting different architectures, without requiring changes in the source code of the application. We present a technique to automatically identify necessary communication based on higher-level semantics. The extracted information enables tools to generate code that handles the communication. A prototype is developed to implement the techniques and used to evaluate the approach. The results show the effectiveness of using the techniques to scale code on multi-core processors and on GPU based machines. Comparing the ratios of the achieved GFLOPS to the number of nodes in each run, and repeating that on different numbers of nodes shows that the achieved scaling efficiency is around 100%. This was repeated with up to 100 nodes. An exception to this is the single-node configuration using a GPU, in which no communication is needed, and hence, no data movement between GPU and host memory is needed, which yields higher GFLOPS

An MPI-IO In-Memory driver for non-volatile pooled memory of the Kove XPD

Many scientific applications are limited by the performance offered by parallel file systems. SSD based burst buffers provide significant better performance than HDD backed storage but at the expense of capacity. Clearly, achieving wire-speed of the interconnect and predictable low latency I/O is the holy grail of storage. In-memory storage promises to provide optimal performance exceeding SSD based solutions. Kove®’s XPD® offers pooled memory for cluster systems. This remote memory is asynchronously backed up to storage devices of the XPDs and considered to be non-volatile. Albeit the system offers various APIs to access this memory such as treating it as a block device, it does not allow to expose it as file system that offers POSIX or MPI-IO semantics. In this paper, we (1) describe the XPD-MPIIO-driver which supports the scale-out architecture of the XPDs. This MPI-agnostic driver enables high-level libraries to utilize the XPD’s memory as storage. (2) A thorough performance evaluation of the XPD is conducted. This includes scale-out testing of the infrastructure and “metadata” operations but also performance variability. We show that the driver and storage architecture is able to nearly saturate wire-speed of Infiniband (60+ GiB/s with 14FDR links) while providing low latency and little performance variability

Benefit of DDN's IME-FUSE for I/O intensive HPC applications

Many scientific applications are limited by I/O performance offered by parallel file systems on conventional storage systems. Flash- based burst buffers provide significant better performance than HDD backed storage, but at the expense of capacity. Burst buffers are consid- ered as the next step towards achieving wire-speed of interconnect and providing more predictable low latency I/O, which are the holy grail of storage. A critical evaluation of storage technology is mandatory as there is no long-term experience with performance behavior for particular applica- tions scenarios. The evaluation enables data centers choosing the right products and system architects the integration in HPC architectures. This paper investigates the native performance of DDN-IME, a flash- based burst buffer solution. Then, it takes a closer look at the IME-FUSE file systems, which uses IMEs as burst buffer and a Lustre file system as back-end. Finally, by utilizing a NetCDF benchmark, it estimates the performance benefit for climate applications

Cost and performance modeling for Earth system data management and beyond

Current and anticipated storage environments confront domain scientist and data center operators with usability, performance and cost challenges. The amount of data upcoming system will be required to handle is expected to grow exponentially, mainly due to increasing resolution and affordable compute power. Unfortunately, the relationship between cost and performance is not always well understood requiring considerable effort for educated procurement. Within the Centre of Excellence in Simulation of Weather and Climate in Europe (ESiWACE) models to better understand cost and performance of current and future systems are being explored. This paper presents models and methodology focusing on, but not limited to, data centers used in the context of climate and numerical weather prediction. The paper concludes with a case study of alternative deployment strategies and outlines the challenges anticipating their impact on cost and performance. By publishing these early results, we would like to make the case to work towards standard models and methodologies collaboratively as a community to create sufficient incentives for vendors to provide specifications in formats which are compatible to these modeling tools. In addition to that, we see application for such formalized models and information in I/O re lated middleware, which are expected to make automated but reasonable decisions in increasingly heterogeneous data centers

Simulation of hierarchical storage systems for TCO and QoS

Due to the variety of storage technologies deep storage hierarchies turn out to be the most feasible choice to meet performance and cost requirements when handling vast amounts of data. Long-term archives employed by scientific users are mainly reliant on tape storage, as it remains the most cost-efficient option. Archival systems are often loosely integrated into the HPC storage infrastructure. In expectation of exascale systems and in situ analysis also burst buffers will require integration with the archive. Exploring new strategies and developing open software for tape systems is a hurdle due to the lack of affordable storage silos and availability outside of large organizations and due to increased wariness requirements when dealing with ultra-durable data. Lessening these problems by providing virtual storage silos should enable community-driven innovation and enable site operators to add features where they see fit while being able to verify strategies before deploying on production systems. Different models for the individual components in tape systems are developed. The models are then implemented in a prototype simulation using discrete event simulation. The work shows that the simulations can be used to approximate the behavior of tape systems deployed in the real world and to conduct experiments without requiring a physical tape system

Predicting performance of non-contiguous I/O with machine learning

Data sieving in ROMIO promises to optimize individual non-contiguous I/O. However, making the right choice and parameterizing its buffer size accordingly are non-trivial tasks, since predicting the resulting performance is difficult. Since many performance factors are not taken into account by data sieving, extracting the optimal performance for a given access pattern and system is often not possible. Additionally, in Lustre, settings such as the stripe size and number of servers are tunable, yet again, identifying rules for the data-centre proves challenging indeed. In this paper, we (1) discuss limitations of data sieving, (2) apply machine learning techniques to build a performance predictor, and (3) learn and extract best practices for the settings from the data. We used decision trees as these models can capture non-linear behavior, are easy to understand and allow for extraction of the rules used. Even though this initial research is based on decision trees, with sparse training data, the algorithm can predict many cases sufficiently. Compared to a standard setting, the decision trees created are able to improve performance significantly and we can derive expert knowledge by extracting rules from the learned tree. Applying the scheme to a set of experimental data improved the average throughput by 25–50 % of the best parametrization’s gain. Additionally, we demonstrate the versatility of this approach by applying it to the porting system of DKRZ’s next generation supercomputer and discuss achievable performance gains

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

Predicting I/O performance in HPC using artificial neural networks

The prediction of file access times is an important part for the modeling of supercomputer's storage systems. These models can be used to develop analysis tools which support the users to integrate efficient I/O behavior. In this paper, we analyze and predict the access times of a Lustre file system from the client perspective. Therefore, we measure file access times in various test series and developed different models for predicting access times. The evaluation shows that in models utilizing artificial neural networks the average prediciton error is about 30% smaller than in linear models. A phenomenon in the distribution of file access times is of particular interest: File accesses with identical parameters show several typical access times.The typical access times usually differ by orders of magnitude and can be explained with a different processing of the file accesses in the storage system - an alternative I/O path. We investigate a method to automatically determine the alternative I/O path and quantify the significance of knowledge about the internal processing. It is shown that the prediction error is improved significantly with this approach