731 research outputs found
funcX: A Federated Function Serving Fabric for Science
Exploding data volumes and velocities, new computational methods and
platforms, and ubiquitous connectivity demand new approaches to computation in
the sciences. These new approaches must enable computation to be mobile, so
that, for example, it can occur near data, be triggered by events (e.g.,
arrival of new data), be offloaded to specialized accelerators, or run remotely
where resources are available. They also require new design approaches in which
monolithic applications can be decomposed into smaller components, that may in
turn be executed separately and on the most suitable resources. To address
these needs we present funcX---a distributed function as a service (FaaS)
platform that enables flexible, scalable, and high performance remote function
execution. funcX's endpoint software can transform existing clouds, clusters,
and supercomputers into function serving systems, while funcX's cloud-hosted
service provides transparent, secure, and reliable function execution across a
federated ecosystem of endpoints. We motivate the need for funcX with several
scientific case studies, present our prototype design and implementation, show
optimizations that deliver throughput in excess of 1 million functions per
second, and demonstrate, via experiments on two supercomputers, that funcX can
scale to more than more than 130000 concurrent workers.Comment: Accepted to ACM Symposium on High-Performance Parallel and
Distributed Computing (HPDC 2020). arXiv admin note: substantial text overlap
with arXiv:1908.0490
Overview of Caching Mechanisms to Improve Hadoop Performance
Nowadays distributed computing environments, large amounts of data are
generated from different resources with a high velocity, rendering the data
difficult to capture, manage, and process within existing relational databases.
Hadoop is a tool to store and process large datasets in a parallel manner
across a cluster of machines in a distributed environment. Hadoop brings many
benefits like flexibility, scalability, and high fault tolerance; however, it
faces some challenges in terms of data access time, I/O operation, and
duplicate computations resulting in extra overhead, resource wastage, and poor
performance. Many researchers have utilized caching mechanisms to tackle these
challenges. For example, they have presented approaches to improve data access
time, enhance data locality rate, remove repetitive calculations, reduce the
number of I/O operations, decrease the job execution time, and increase
resource efficiency. In the current study, we provide a comprehensive overview
of caching strategies to improve Hadoop performance. Additionally, a novel
classification is introduced based on cache utilization. Using this
classification, we analyze the impact on Hadoop performance and discuss the
advantages and disadvantages of each group. Finally, a novel hybrid approach
called Hybrid Intelligent Cache (HIC) that combines the benefits of two methods
from different groups, H-SVM-LRU and CLQLMRS, is presented. Experimental
results show that our hybrid method achieves an average improvement of 31.2% in
job execution time
MapReduce analysis for cloud-archived data
Public storage clouds have become a popular choice for archiving certain classes of enterprise data - for example, application and infrastructure logs. These logs contain sensitive information like IP addresses or user logins due to which regulatory and security requirements often require data to be encrypted before moved to the cloud. In order to leverage such data for any business value, analytics systems (e.g. Hadoop/MapReduce) first download data from these public clouds, decrypt it and then process it at the secure enterprise site. We propose VNCache: an efficient solution for MapReduceanalysis of such cloud-archived log data without requiring an apriori data transfer and loading into the local Hadoop cluster. VNcache dynamically integrates cloud-archived data into a virtual namespace at the enterprise Hadoop cluster. Through a seamless data streaming and prefetching model, Hadoop jobs can begin execution as soon as they are launched without requiring any apriori downloading. With VNcache's accurate pre-fetching and caching, jobs often run on a local cached copy of the data block significantly improving performance. When no longer needed, data is safely evicted from the enterprise cluster reducing the total storage footprint. Uniquely, VNcache is implemented with NO changes to the Hadoop application stack. © 2014 IEEE
A Quantitative Approach for Adopting Disaggregated Memory in HPC Systems
Memory disaggregation has recently been adopted in data centers to improve
resource utilization, motivated by cost and sustainability. Recent studies on
large-scale HPC facilities have also highlighted memory underutilization. A
promising and non-disruptive option for memory disaggregation is rack-scale
memory pooling, where shared memory pools supplement node-local memory. This
work outlines the prospects and requirements for adoption and clarifies several
misconceptions. We propose a quantitative method for dissecting application
requirements on the memory system from the top down in three levels, moving
from general, to multi-tier memory systems, and then to memory pooling. We
provide a multi-level profiling tool and LBench to facilitate the quantitative
approach. We evaluate a set of representative HPC workloads on an emulated
platform. Our results show that prefetching activities can significantly
influence memory traffic profiles. Interference in memory pooling has varied
impacts on applications, depending on their access ratios to memory tiers and
arithmetic intensities. Finally, in two case studies, we show the benefits of
our findings at the application and system levels, achieving 50% reduction in
remote access and 13% speedup in BFS, and reducing performance variation of
co-located workloads in interference-aware job scheduling.Comment: Accepted to SC23 (The International Conference for High Performance
Computing, Networking, Storage, and Analysis 2023
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