Resource disaggregation is a promising technique for improving the efficiency of large-scale computing systems.However, this comes at the cost of increased memory access latency due to the need to rely on the network fabric to transfer data between remote nodes.As such, it is crucial to ascertain an application's memory latency sensitivity to minimize the overall performance impact.Existing tools for measuring memory latency sensitivity often rely on custom ad-hoc hardware or cycle-accurate simulators, which can be inflexible and time-consuming.To address this, we present EDAN (Execution DAG Analyzer), a novel performance analysis tool that leverages an application's runtime instruction trace to generate its corresponding execution DAG.This approach allows us to estimate the latency sensitivity of sequential programs and investigate the impact of different hardware configurations.EDAN not only provides us with the capability of calculating the theoretical bounds for performance metrics, but it also helps us gain insight into the memorylevel parallelism inherent to HPC applications.We apply

The shift towards high-bandwidth networks driven by AI workloads in data centers and HPC clusters has unintentionally aggravated network latency, adversely affecting the performance of communication-intensive HPC applications. As large-scale applications often exhibit significant differences in their network latency tolerance, it is crucial to determine the extent of network latency an application can withstand without significant performance degradation. Current approaches often rely on specialized hardware or simulators, which can be inflexible and time-consuming. We introduce LLAMP, a novel toolchain that offers an efficient analytical approach to evaluating HPC applications’ network latency tolerance using the LogGPS model and linear programming. Through our validation on a variety of MPI applications such as LULESH and MILC, we demonstrate our tool’s high accuracy, with relative prediction errors below 2%. Additionally, we include a case study of the ICON weather and climate model to illustrate LLAMP’s broad applicability in evaluating collective algorithms and network topologies.

There has been a dramatic increase in the popularity of Container as a Service (CaaS) clouds. The CaaS multi-tier applications could be optimized by using network topology, link or server load knowledge to choose the best endpoints to run in CaaS cloud. However, it is difficult to apply those optimizations to the public datacenter shared by multi-tenants. This is because of the opacity between the tenants and the datacenter providers: Providers have no insight into tenant's container workloads and dependencies, while tenants have no clue about the underlying network topology, link, and load. As a result, containers might be booted at wrong physical nodes that lead to performance degradation due to bi-section bandwidth bottleneck or co-located container interference. We propose 'DocMan', a toolset that adopts a black-box approach to discover container ensembles and collect information about intra-ensemble container interactions. It uses a combination of techniques such as distance identification and hierarchical clustering. The experimental results demonstrate that DocMan enables optimized containers placement to reduce the stress on bi-section bandwidth of the datacenter's network. The method can detect container ensembles at low cost and with 92% accuracy and significantly improve performance for multi-tier applications under the best of circumstances.

Web-based social networks enable new community-based opportunities for participants to engage, share their thoughts, and interact with each other. Theses related activities such as searching and advertising are threatened by spammers, content polluters, and malware disseminators. We propose a scalable spam detection system, termed Oases, for uncovering social spam in social networks using an online and scalable approach. The novelty of our design lies in two key components: (1) a decentralized DHT-based tree overlay deployment for harvesting and uncovering deceptive spam from social communities; and (2) a progressive aggregation tree for aggregating the properties of these spam posts for creating new spam classifiers to actively filter out new spam. We design and implement the prototype of Oases and discuss the design considerations of the proposed approach. Our large-scale experiments using real-world Twitter data demonstrate scalability, attractive load-balancing, and graceful efficiency in online spam detection for social networks.

The two most common parallel execution models for many-core CPUs today are multiprocess (e.g., MPI) and multithread (e.g., OpenMP). The multiprocess model allows each process to own a private address space, although processes can explicitly allocate shared-memory regions. The multithreaded model shares all address space by default, although threads can explicitly move data to thread-private storage. In this paper, we present a third model called process-in-process (PiP), where multiple processes are mapped into a single virtual address space. Thus, each process still owns its process-private storage (like the multiprocess model) but can directly access the private storage of other processes in the same virtual address space (like the multithread model).

This poster describes our work on SuperGlue, a set of generic, reusable components for composing scientific workflows. These are distributed data analysis and manipulation tools that can be chained together to form a variety of real-time workflows providing analytical results during the execution of the primary scientific code. Unlike existing components used in IAWs, SuperGlue components do not have a fixed data type. This one change enables using these components on completely different kinds of simulations that share nothing in their output format. Key to making this work is using a typed transport mechanism between different components. Many options exist for these transports and the particular mechanism selected is not critical.

As scientific simulation applications evolve on the path towards exascale, a new model of scientific inquiry is required where concurrently with the running simulation, online analytics operate on the data it produces. By avoiding offline data storage except when absoluately necessary, it enables speeding up the scientific discovery process by providing rapid insights into the simulated science phenomena and affording more frequent, detailed data analytics than is possible with the traditional purely offline approach of using disk for intermediate data storage. However, a challenge for online analytics is to respond to behavior dynamics caused by changing simulation outputs and by unforeseen events on the underlying hardware/software platforms. This paper presents SODA, a set of run-time abstractions for online orchestration of data analytics, realized by embedding analytics tasks into workstations that monitor component behavior and enable responses to run-time changes in their resource demands and in the platform's resource availability. For high end simulations running on a leadership class machine, experimental evaluations show SODA can invoke efficient orchestration operations responding to a diverse set of run-time dynamics at different granularities to meet end-user and analysis specific requirements.

As high-end systems move toward exascale sizes, a new model of scientific inquiry being developed is one in which online data analytics run concurrently with the high end simulations producing data outputs. Goals are to gain rapid insights into the ongoing scientific processes, assess their scientific validity, and/or initiate corrective or supplementary actions by launching additional computations when needed. The Flex path system presented in this paper addresses the fundamental problem of how to structure and efficiently implement the communications between high end simulations and concurrently running online data analytics, the latter comprised of componentized dynamic services and service pipelines. Using a type-based publish/subscribe approach, Flexpath encourages diversity by permitting analytics services to differ in their computational and scaling characteristics and even in their internal execution models. Flex path uses direct and MxN connections between interacting services to reduce data movements, to allow for runtime connectivity changes to accommodate component arrivals/departures, and to support the multiple underlying communication protocols used for analytics workflows in which simulation outputs are processed by analytics services residing on the same nodes where they are generated, on the same machine, and/or on attached or remote analytics engines. This paper describes the design and implementation of Flex path, and evaluates it with two widely used scientific applications and their associated data analytics methods.

The remote visual exploration of live data generated by scientific simulations is useful for scientific discovery, performance monitoring, and online validation for the simulation results. Online visualization methods are challenged, however, by the continued growth in the volume of simulation output data that has to be transferred from its source - the simulation running on the high end machine - to where it is analyzed, visualized, and displayed. A specific challenge in this context is limits in the communication bandwidth between data source(s) and sinks. Previous work places queries `near' data sources, exploiting their data reduction capabilities, but such work does not address the common scenario in which scientists make multiple different queries on the data being produced. This paper considers the general case in which science users are interested in different (sub)sets of the data produced by a high end simulation. We offer the FlexQuery online data query system that can deploy and execute data queries `along' the I/O and analytics pipelines. FlexQuery carefully extends such analytics pipelines, using online performance monitoring and data location tracking, to realize data queries in ways that minimize additional data movement and offer low latency in data query execution. Using a real-world scientific application - the Maya astrophysics code and its analytics workflow - we demonstrate FlexQuery's ability to dynamically deploy queries for low-latency remote data visualization.

Increasingly severe I/O bottlenecks on High-End Computing machines are prompting scientists to process simulation output data online while simulations are running and before storing data on disk. There are several options to place data analytics along the I/O path: on compute nodes, on separate nodes dedicated to analytics, or after data is stored on persistent storage. Since different placements have different impact on performance and cost, there is a consequent need for flexibility in the location of data analytics. The FlexIO middleware described in this paper makes it easy for scientists to obtain such flexibility, by offering simple abstractions and diverse data movement methods to couple simulation with analytics. Various placement policies can be built on top of FlexIO to exploit the trade-offs in performing analytics at different levels of the I/O hierarchy. Experimental results demonstrate that FlexIO can support a variety of simulation and analytics workloads at large scale through flexible placement options, efficient data movement, and dynamic deployment of data manipulation functionalities.

Current exascale computing projections suggest rather than a monolithic simulation running for the majority of the machine, a collection of components comprising the scientific discovery process will be employed in an online workflow. This move to an online workflow scenario requires knowledge that inter-step operations are completed and correct before the next phase begins. Further, dynamic load balancing or fault tolerance techniques may dynamically deploy or redeploy resources for optimal use of computing resources. These newly configured resources should only be used if they are successfully deployed. Our D2T system offers a mechanism to support these kinds of operations by providing database-like transactions with distributed servers and clients. Ultimately, with adequate hardware support, full ACID compliance is possible for the transactions. To prove the viability of this approach, we show that the D2T protocol has less than 1.2 seconds of overhead using 4096 clients and 32 servers with good scaling characteristics using this initial prototype implementation.

Abstract Electronic Health Records (EHRs) have many potential advantages over traditional paper records, such as wide scale access, error checking, and protection from physical damage to a record. As with any medical record, paper or electronic, both the patient's privacy and the document's integrity must be guaranteed. With initiatives such as Integrating the Healthcare Enterprise (IHE), computerized healthcare systems are able to share EHRs on a large scale, while protecting the patient's privacy rights. However, IHE does not yet meet the needs of all healthcare systems, as we will show with the eMOLST project. The eMOLST project delivers software in support of Medical Order for Life Sustaining Treatment (MOLST) forms and uses IHE specifications for cross enterprise document storage and sharing, patient identification, and user authentication and authorization. The Web‐based system provides secure access to electronic MOLST documents regardless of the patient's or healthcare provider's location. The eMOLST project allows a user to have Single Sign On (SSO) access to the system from either the user's associated enterprise, or through a Web portal shared amongst all users across all enterprises. In this paper, we show a security solution to allow SSO from multiple access points for IHE compliant systems. Copyright © 2011 John Wiley & Sons, Ltd.

Reducing energy consumption for high end computing can bring various benefits such as, reduce operating costs, increase system reliability, and environment respect. This paper aims to develop scheduling heuristics and to present application experience for reducing power consumption of parallel tasks in a cluster with the Dynamic Voltage Frequency Scaling (DVFS) technique. In this paper, formal models are presented for precedence-constrained parallel tasks, DVFS enabled clusters, and energy consumption. This paper studies the slack time for non-critical jobs, extends their execution time and reduces the energy consumption without increasing the task’s execution time as a whole. Additionally, Green Service Level Agreement is also considered in this paper. By increasing task execution time within an affordable limit, this paper develops scheduling heuristics to reduce energy consumption of a tasks execution and discusses the relationship between energy consumption and task execution time. Models and scheduling heuristics are examined with a simulation study. Test results justify the design and implementation of proposed energy aware scheduling heuristics in the paper.

Data centers now play an important role in modern IT infrastructures. Related research has shown that the energy consumption for data center cooling systems has recently increased significantly. There is also strong evidence to show that high temperatures with in a data center will lead to higher hardware failure rates and thus an increase in maintenance costs. This paper devotes itself in the field of thermal aware resource management for data centers. This paper proposes an analytical model, which describes data center resources with heat transfer properties and workloads with thermal features. Then a thermal aware task scheduling algorithm with backfilling is presented which aims to reduce power consumption and temperatures in a data center. A simulation study is carried out to evaluate the performance of the algorithm. Simulation results show that our algorithm can significantly reduce temperatures in data centers by introducing endurable decline in performance.