Active Flash: Out-of-core data analytics on flash storage

In the UNIX/Linux environment the kernel can log every command process created by every user with process accounting. Thus process accounting logs have many potential uses, particularly the monitoring and forensic investigation of security events. Previous work successfully leveraged the use of process accounting logs to identify a difficult to detect and damaging intrusions within high performance computing (HPC) clusters, masquerade attacks, where intruders pose as legitimate users with purloined authentication credentials. This paper incrementally advances the goal of more accurately identifying masqueraders on HPC clusters by seeking to identify features within command sets that distinguish masqueraders. To accomplish this goal, we created NVision-PA, a software tool which produces text and graphic statistical summaries describing input processing accounting logs. This research is both a promising next step toward creating a real-time masquerade detection sensor for production HPC clusters as well as providing another tool for system administrators to use for statistically monitoring and managing legitimate workloads in HPC environments

HDFS (Hadoop Distributed File System) is the primary storage of Hadoop. Even though data locality offered by HDFS is important for Big Data applications, HDFS suffers from huge I/O bottlenecks due to the tri-replicated data blocks and cannot efficiently utilize the available storage devices in an HPC (High Performance Computing) cluster. Moreover, due to the limitation of local storage space, it is challenging to deploy HDFS in HPC environments. In this paper, we present a hybrid design (Triple-H) that can minimize the I/O bottlenecks in HDFS and ensure efficient utilization of the heterogeneous storage devices (e.g. RAM, SSD, and HDD) available on HPC clusters. We also propose effective data placement policies to speed up Triple-H. Our design integrated with parallel file system (e.g. Lustre) can lead to significant storage space savings and guarantee fault-tolerance. Performance evaluations show that Triple-H can improve the write and read throughputs of HDFS by up to 7x and 2x, respectively. The execution times of data generation benchmarks are reduced by up to 3x. Our design also improves the execution time of the Sort benchmark by up to 40% over default HDFS and 54% over Lustre. The alignment phase of the Cloudburst application is accelerated by 19%. Triple-H also benefits the performance of SequenceCount and Grep in PUMA over both default HDFS and Lustre.

Data-intensive computing has become one of the major workloads on traditional high-performance computing (HPC) clusters. Currently, deploying data-intensive computing software framework on HPC clusters still faces performance and scalability issues. In this paper, we develop a new two-level storage system by integrating Tachyon, an in-memory file system with OrangeFS, a parallel file system. We model the I/O throughputs of four storage structures: HDFS, OrangeFS, Tachyon and two-level storage. We conduct computational experiments to characterize I/O throughput behavior of two-level storage and compare its performance to that of HDFS and OrangeFS, using TeraSort benchmark. Theoretical models and experimental tests both show that the two-level storage system can increase the aggregate I/O throughputs. This work lays a solid foundation for future work in designing and building HPC systems that can provide a better support on I/O intensive workloads with preserving existing computing resources.

This Research to Practice Full Paper presents the experience of implementing micro HPC (μHPC) clusters in the education context. In developing countries, access to and experience of High Performance Computing (HPC) facilities (which are important for the economic development of those countries) has presented challenges to academic institutions due to prohibitive costs of acquisition and maintenance of HPC systems. These challenges affect the delivery of courses, which include HPC and parallel computation in their syllabi. However, due to advancements in integrated circuit technologies, credit-card-sized personal computers (PCs) have emerged with the capabilities of a fully-fledged PC. These credit-card-sized PCs offer an opportunity for system integrators to build micro HPC (μHPC) clusters which, apart from their efficiency, scalability and availability, have a number of attributes which are not shared with conventional data-center based HPC clusters These include their low power consumption, portability, diskless nodes, cost-effectiveness and excellent price to performance ratios. μHPC clusters are, therefore, ideally suited to support curricula in all educational disciplines that demand parallel computation. The purpose of this study is to investigate the implementation of μHPC to find out from students how ease of use of a μHPC system is to acquire skills and knowledge of HPC. We used focus groups and surveys to gather data in the framework of the design science research paradigm. The implementation of μHPC clusters involved a demonstration of the system in the problem context that provided valuable lessons. The results indicate that μHPC is the ease of use HPC artifact for learning about management, integration, deployment, installation, downloading, and running of parallel programs.

Contention for shared resources in High-Performance Computing (HPC) clusters occurs when jobs are concurrently executing on the same multicore node (there is a contention for shared caches, memory buses, memory controllers and memory domains). The shared resource contention incurs severe degradation to workload performance and stability and hence must be addressed. The state-of-the-art HPC clusters, however, are not contention-aware. The goal of this work is the design, implementation and evaluation of a virtualized HPC cluster framework that is contention aware.

We benchmarked the performance of the GROMACS 2024 molecular dynamics (MD) code on a modern high-performance computing (HPC) cluster with AMD CPUs on up to 65,536 CPU cores. We used five different MD systems, ranging in size from about 82,000 to 204 million atoms, and evaluated their performance using two different Message Passing Interface (MPI) libraries, Intel-MPI and Open-MPI. The largest system showed near-perfect strong scaling up to 512 nodes or 65,536 cores, maintaining a parallel efficiency above 0.9 even at the highest level of parallelization. Energy efficiency for a given number of nodes was generally equal to or slightly better than parallel efficiency. We achieved peak performances of 687 ns/d for the 82k atom system, 116 ns/d for the 53M atom system, and about 35 ns/d for the largest 204M atom system. These results demonstrate that highly optimized software running on a state-of-the-art HPC cluster provides sufficient computing power to simulate biomolecular systems at the mesoscale of viruses and organelles, and potentially small cells in the near future.

Summary form only given. The effect of Intel/spl reg/ hyper-threading (HT) technology on a system's performance varies according to the characteristics of the application running on the system and the configuration of the system. High performance computing (HPC) clusters introduce additional variables, such as the math libraries used in solving linear algebra equations that can affect the performance of scientific and engineering applications in particular. We study the effect of HT on MPI-based applications by varying the math library. We configure an Intel-based HPC cluster and used the High Performance Linpack (HPL) benchmark to study the performance characteristics without changing hardware or communication parameters, but by linking the application with different math libraries. What we have found is that, even though the application or hardware parameters remain the same, HT may help or hinder the overall performance of the cluster depending on the computational efficiency of the mathematical functions.

The lightweight nature, application portability, and deployment flexibility of containers is driving their widespread adoption in cloud solutions. Data analysis and deep learning (DL)/machine learning (ML) applications have especially benefited from containerization. As such data analysis is adopted in high performance computing (HPC), the need for container support in HPC has become paramount. However, containers face crucial performance and I/O challenges in HPC. One obstacle is that while there have been HPC containers, such solutions have not been thoroughly investigated, especially from the aspect of their impact on the crucial HPC I/O throughput. To this end, this paper provides a first-of-its-kind empirical analysis of state-of-the-art representative container solutions (Docker, Podman, Singularity, and Charliecloud) in HPC environments. We also explore how containers interact with an HPC parallel file system like Lustre. We present the design of an analysis framework that is deployed on all nodes in an HPC environment, and captures CPU, memory, network, and file I/O statistics from the nodes and the storage system. We are able to garner key insights from our analysis, e.g., Charliecloud outperforms other container solutions in terms of container start-up time, while Singularity and Charliecloud are equivalent in I/O throughput. But this comes at a cost, as Charliecloud invokes the most metadata and I/O operations on the underlying Lustre file system. By identifying such trade-offs and optimization opportunities, we can enhance HPC containers performance and the ML/DL applications that increasingly rely on them.

Understanding data can be tricky and sometimes overwhelming. Using automation helps a data analysis team improve their methods more consistently. Specifically, automating software tasks is the best way to manage the many repetitions and repetitive jobs that come with thorough data analysis. When starting a project that involves software, automation, and working together, DevOps is the best way to go. DevOps improves all parts of the data analysis process, allowing teams to automatically handle software-related tasks in data analysis and work well with everyone involved in the project. The combination of DevOps, Data Analytics, and Data Engineering introduced the new DataOps which is used for data processing and analytics. DataOps improves communication, collaboration, and faster data pipeline

High-Performance Computing (HPC) systems consume large amounts of energy. As the energy consumption predictions for HPC show increasing numbers, it is important to make users aware of the energy spent for the execution of their applications. Drawing from our experience with exposing cost and performance in public clouds, in this paper we present a generic mechanism to compute fast and accurate estimates for the tradeoffs between the performance (expressed as makespan) and the energy consumption of applications running on HPC clusters. We validate our approach by implementing it in a prototype, called E-BaTS and validating it with a wide variety of HPC bags-of-tasks. Our experiments show that E-BaTS produces conservative estimates with errors below 5%, while requiring at most 12% of the energy and time of an exhaustive search for providing configurations close to the optimal ones in terms of trade-offs between energy consumption and makespan.

The complexity of current Deep learning has been growing rapidly nowadays. Such advancement allows various organizations such as private sectors and government to leverage intelligent systems on their use cases. High Performance Computing (HPC) infrastructure nowadays has pivoted to GPU-oriented systems, enabling developers and researchers to train complex models with large datasets unlike conventional clusters equipped only with CPU cores. However, focus on power efficiency on the HPC system has not been prevalent especially on the new system such as DGX A100 that does not have datapoints on how GPUs consumed power. Even though such HPC cluster can be powerful, always allowing it to run at the maximum capacity results to financial cost to the HPC provider at the end. Therefore, for any organization providing the system, it is crucial for them to balance the cluster capabilities while maintaining overall power consumption which can potentially be costly in the long term. This paper reveals A100 GPU metrics that are relevant to Power usage and explains GPU profiling applied to Deep learning workload on the cluster, saving up to 32% of the power usage while compromising only 11.5% of training time compared to a default profile. Then, the paper investigates literature review that could be learned further adopted to the current system at CMKL university as the next milestone.

In this paper, we comprehensively explain how we built a novel implementation of the Random Forest algorithm on the High Performance Computing Cluster (HPCC) Systems Platform from LexisNexis. The algorithm was previously unavailable on that platform. Random Forest’s learning process is based on the principle of recursive partitioning and although recursion per se is not allowed in ECL (HPCC’s programming language), we were able to implement the recursive partition algorithm as an iterative split/partition process. In addition, we analyze the flaws found in our initial implementation and we thoroughly describe all the modifications required to overcome the bottleneck within the iterative split/partition process, i.e., the optimization of the data gathering of selected independent variables which are used for the node’s best-split analysis. Essentially, we describe how our initial Random Forest implementation has been optimized and has become an efficient distributed machine learning implementation for Big Data. By taking full advantage of the HPCC Systems Platform’s Big Data processing and analytics capabilities, we succeed in enhancing the data gathering method from an inefficient Pass them All and Filter approach into an effective and completely parallelized Fetching on Demand approach. Finally, based upon the results of our learning process runtime comparison between these two approaches, we confirm the speed up of our optimized Random Forest implementation.

For decades, HPC has established itself as an essential tool for discoveries, innovations and new insights in science, research and development, engineering and business across a wide range of application areas in academia and industry. Today High-Performance Computing is also well recognized to be of strategic and economic value – HPC matters and is transforming industries. This article will discuss new emerging technologies that are being developed for all areas of HPC: compute/processing, memory and storage, interconnect fabric, I/O and software to address the ongoing challenges in HPC such as balanced architecture, energy efficient high-performance, density, reliability, sustainability, and last but not least ease-of-use. Of specific interest are the challenges and opportunities for the next frontier in HPC envisioned around the 2020 timeframe: ExaFlops computing. We will also outline the new and emerging area of High Performance Data Analytics, Big Data Analytics using HPC, and discuss the emerging new delivery mechanism for HPC - HPC in the Cloud.

Chapter 4 - Frameworks for Big Data Integration, Warehousing, and Analytics

Analysis and evaluation of MapReduce solutions on an HPC cluster