Benchmarking adaptive indexing

Ideally, realizing the best physical design for the current and all subsequent workloads would impact neither performance nor storage usage. In reality, workloads and datasets can change dramatically over time and index creation impacts the performance of concurrent user and system activity. We propose a framework that evaluates the key premise of adaptive indexing --- a new indexing paradigm where index creation and re-organization take place automatically and incrementally, as a side-effect of query execution. We focus on how the incremental costs and benefits of dynamic reorganization are distributed across the workload's lifetime. We believe measuring the costs and utility of the stages of adaptation are relevant metrics for evaluating new query processing paradigms and comparing them to traditional approaches

Load-Varying LINPACK: A Benchmark for Evaluating Energy Efficiency in High-End Computing

For decades, performance has driven the high-end computing (HEC) community. However, as highlighted in recent exascale studies that chart a path from petascale to exascale computing, power consumption is fast becoming the major design constraint in HEC. Consequently, the HEC community needs to address this issue in future petascale and exascale computing systems. Current scientific benchmarks, such as LINPACK and SPEChpc, only evaluate HEC systems when running at full throttle, i.e., 100% workload, resulting in a focus on performance and ignoring the issues of power and energy consumption. In contrast, efforts like SPECpower evaluate the energy efficiency of a compute server at varying workloads. This is analogous to evaluating the energy efficiency (i.e., fuel efficiency) of an automobile at varying speeds (e.g., miles per gallon highway versus city). SPECpower, however, only evaluates the energy efficiency of a single compute server rather than a HEC system; furthermore, it is based on SPEC's Java Business Benchmarks (SPECjbb) rather than a scientific benchmark. Given the absence of a load-varying scientific benchmark to evaluate the energy efficiency of HEC systems at different workloads, we propose the load-varying LINPACK (LV-LINPACK) benchmark. In this paper, we identify application parameters that affect performance and provide a methodology to vary the workload of LINPACK, thus enabling a more rigorous study of energy efficiency in supercomputers, or more generally, HEC

BDEv 3.0: energy efficiency and microarchitectural characterization of Big Data processing frameworks

This is a post-peer-review, pre-copyedit version of an article published in Future Generation Computer Systems. The final authenticated version is available online at: https://doi.org/10.1016/j.future.2018.04.030[Abstract] As the size of Big Data workloads keeps increasing, the evaluation of distributed frameworks becomes a crucial task in order to identify potential performance bottlenecks that may delay the processing of large datasets. While most of the existing works generally focus only on execution time and resource utilization, analyzing other important metrics is key to fully understanding the behavior of these frameworks. For example, microarchitecture-level events can bring meaningful insights to characterize the interaction between frameworks and hardware. Moreover, energy consumption is also gaining increasing attention as systems scale to thousands of cores. This work discusses the current state of the art in evaluating distributed processing frameworks, while extending our Big Data Evaluator tool (BDEv) to extract energy efficiency and microarchitecture-level metrics from the execution of representative Big Data workloads. An experimental evaluation using BDEv demonstrates its usefulness to bring meaningful information from popular frameworks such as Hadoop, Spark and Flink.Ministerio de Economía, Industria y Competitividad; TIN2016-75845-PMinisterio de Educación; FPU14/02805Ministerio de Educación; FPU15/0338

Evaluating the benefits of key-value databases for scientific applications

The convergence of Big Data applications with High-Performance Computing requires new methodologies to store, manage and process large amounts of information. Traditional storage solutions are unable to scale and that results in complex coding strategies. For example, the brain atlas of the Human Brain Project has the challenge to process large amounts of high-resolution brain images. Given the computing needs, we study the effects of replacing a traditional storage system with a distributed Key-Value database on a cell segmentation application. The original code uses HDF5 files on GPFS through an intricate interface, imposing synchronizations. On the other hand, by using Apache Cassandra or ScyllaDB through Hecuba, the application code is greatly simplified. Thanks to the Key-Value data model, the number of synchronizations is reduced and the time dedicated to I/O scales when increasing the number of nodes.This project/research has received funding from the European Unions Horizon 2020 Framework Programme for Research and Innovation under the Speci c Grant Agreement No. 720270 (Human Brain Project SGA1) and the Speci c Grant Agreement No. 785907 (Human Brain Project SGA2). This work has also been supported by the Spanish Government (SEV2015-0493), by the Spanish Ministry of Science and Innovation (contract TIN2015-65316-P), and by Generalitat de Catalunya (contract 2017-SGR-1414).Postprint (author's final draft

Evaluation of Docker Containers for Scientific Workloads in the Cloud

The HPC community is actively researching and evaluating tools to support execution of scientific applications in cloud-based environments. Among the various technologies, containers have recently gained importance as they have significantly better performance compared to full-scale virtualization, support for microservices and DevOps, and work seamlessly with workflow and orchestration tools. Docker is currently the leader in containerization technology because it offers low overhead, flexibility, portability of applications, and reproducibility. Singularity is another container solution that is of interest as it is designed specifically for scientific applications. It is important to conduct performance and feature analysis of the container technologies to understand their applicability for each application and target execution environment. This paper presents a (1) performance evaluation of Docker and Singularity on bare metal nodes in the Chameleon cloud (2) mechanism by which Docker containers can be mapped with InfiniBand hardware with RDMA communication and (3) analysis of mapping elements of parallel workloads to the containers for optimal resource management with container-ready orchestration tools. Our experiments are targeted toward application developers so that they can make informed decisions on choosing the container technologies and approaches that are suitable for their HPC workloads on cloud infrastructure. Our performance analysis shows that scientific workloads for both Docker and Singularity based containers can achieve near-native performance. Singularity is designed specifically for HPC workloads. However, Docker still has advantages over Singularity for use in clouds as it provides overlay networking and an intuitive way to run MPI applications with one container per rank for fine-grained resources allocation