6,359 research outputs found
Propagation and Decay of Injected One-Off Delays on Clusters: A Case Study
Analytic, first-principles performance modeling of distributed-memory
applications is difficult due to a wide spectrum of random disturbances caused
by the application and the system. These disturbances (commonly called "noise")
destroy the assumptions of regularity that one usually employs when
constructing simple analytic models. Despite numerous efforts to quantify,
categorize, and reduce such effects, a comprehensive quantitative understanding
of their performance impact is not available, especially for long delays that
have global consequences for the parallel application. In this work, we
investigate various traces collected from synthetic benchmarks that mimic real
applications on simulated and real message-passing systems in order to pinpoint
the mechanisms behind delay propagation. We analyze the dependence of the
propagation speed of idle waves emanating from injected delays with respect to
the execution and communication properties of the application, study how such
delays decay under increased noise levels, and how they interact with each
other. We also show how fine-grained noise can make a system immune against the
adverse effects of propagating idle waves. Our results contribute to a better
understanding of the collective phenomena that manifest themselves in
distributed-memory parallel applications.Comment: 10 pages, 9 figures; title change
Idle Period Propagation in Message-Passing Applications
Idle periods on different processes of Message Passing applications are
unavoidable. While the origin of idle periods on a single process is well
understood as the effect of system and architectural random delays, yet it is
unclear how these idle periods propagate from one process to another. It is
important to understand idle period propagation in Message Passing applications
as it allows application developers to design communication patterns avoiding
idle period propagation and the consequent performance degradation in their
applications. To understand idle period propagation, we introduce a methodology
to trace idle periods when a process is waiting for data from a remote delayed
process in MPI applications. We apply this technique in an MPI application that
solves the heat equation to study idle period propagation on three different
systems. We confirm that idle periods move between processes in the form of
waves and that there are different stages in idle period propagation. Our
methodology enables us to identify a self-synchronization phenomenon that
occurs on two systems where some processes run slower than the other processes.Comment: 18th International Conference on High Performance Computing and
Communications, IEEE, 201
Workload Schedulers - Genesis, Algorithms and Comparisons
In this article we provide brief descriptions of three classes of schedulers: Operating Systems Process Schedulers, Cluster Systems, Jobs Schedulers and Big Data Schedulers. We describe their evolution from early adoptions to modern implementations, considering both the use and features of algorithms. In summary, we discuss differences between all presented classes of schedulers and discuss their chronological development. In conclusion, we highlight similarities in the focus of scheduling strategies design, applicable to both local and distributed systems
Project Final Report: HPC-Colony II
This report recounts the HPC Colony II Project which was a computer science effort funded by DOE's Advanced Scientific Computing Research office. The project included researchers from ORNL, IBM, and the University of Illinois at Urbana-Champaign. The topic of the effort was adaptive system software for extreme scale parallel machines. A description of findings is included
A holistic scalability strategy for time series databases following cascading polyglot persistence
Time series databases aim to handle big amounts of data in a fast way, both when introducing new data to the system, and when retrieving it later on. However, depending on the scenario in which these databases participate, reducing the number of requested resources becomes a further requirement. Following this goal, NagareDB and its Cascading Polyglot Persistence approach were born. They were not just intended to provide a fast time series solution, but also to find a great cost-efficiency balance. However, although they provided outstanding results, they lacked a natural way of scaling out in a cluster fashion. Consequently, monolithic approaches could extract the maximum value from the solution but distributed ones had to rely on general scalability approaches. In this research, we proposed a holistic approach specially tailored for databases following Cascading Polyglot Persistence to further maximize its inherent resource-saving goals. The proposed approach reduced the cluster size by 33%, in a setup with just three ingestion nodes and up to 50% in a setup with 10 ingestion nodes. Moreover, the evaluation shows that our scaling method is able to provide efficient cluster growth, offering scalability speedups greater than 85% in comparison to a theoretically 100% perfect scaling, while also ensuring data safety via data replication.This research was partly supported by the Grant Agreement No. 857191, by the Spanish Ministry of Science and Innovation (contract PID2019-107255GB) and by the Generalitat de Catalunya (contract 2017-SGR-1414).Peer ReviewedPostprint (published version
Virtual InfiniBand Clusters for HPC Clouds
High Performance Computing (HPC) employs fast interconnect technologies to provide low communication and synchronization latencies for tightly coupled parallel compute jobs. Contemporary HPC clusters have a xed capacity and static runtime environments; they cannot elastically adapt to dynamic workloads, and provide a limited selection of applications, libraries, and system software. In contrast, a cloud model for HPC clusters promises more exibility, as it provides elastic virtual clusters to be available on-demand. This is not possible with physically owned clusters.
In this paper, we present an approach that makes it possible to use InfiniBand clusters for HPC cloud computing. We propose a performance-driven design of an HPC IaaS layer for In niBand, which provides throughput and latency-aware virtualization of nodes, networks, and network topologies, as well as an approach to an HPC-aware, multi-tenant cloud management system for elastic virtualized HPC compute clusters
Datacenter Traffic Control: Understanding Techniques and Trade-offs
Datacenters provide cost-effective and flexible access to scalable compute
and storage resources necessary for today's cloud computing needs. A typical
datacenter is made up of thousands of servers connected with a large network
and usually managed by one operator. To provide quality access to the variety
of applications and services hosted on datacenters and maximize performance, it
deems necessary to use datacenter networks effectively and efficiently.
Datacenter traffic is often a mix of several classes with different priorities
and requirements. This includes user-generated interactive traffic, traffic
with deadlines, and long-running traffic. To this end, custom transport
protocols and traffic management techniques have been developed to improve
datacenter network performance.
In this tutorial paper, we review the general architecture of datacenter
networks, various topologies proposed for them, their traffic properties,
general traffic control challenges in datacenters and general traffic control
objectives. The purpose of this paper is to bring out the important
characteristics of traffic control in datacenters and not to survey all
existing solutions (as it is virtually impossible due to massive body of
existing research). We hope to provide readers with a wide range of options and
factors while considering a variety of traffic control mechanisms. We discuss
various characteristics of datacenter traffic control including management
schemes, transmission control, traffic shaping, prioritization, load balancing,
multipathing, and traffic scheduling. Next, we point to several open challenges
as well as new and interesting networking paradigms. At the end of this paper,
we briefly review inter-datacenter networks that connect geographically
dispersed datacenters which have been receiving increasing attention recently
and pose interesting and novel research problems.Comment: Accepted for Publication in IEEE Communications Surveys and Tutorial
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