Application heartbeats

Hoffmann, Henry; Eastep, Jonathan; Santambrogio, Marco D.; Miller, Joel B.; Agarwal, Anant

doi:10.1145/1809049.1809065

Cited by 131 publications

(14 citation statements)

References 20 publications

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“…The first paradigm refers to the increasing number of compute units that are integrated on the same chip, enabled today by the 22 and 14 nm technology nodes for processor fabrication. As a result, the Intel Xeon Processor E7 Family provides up to 15 cores 1 and the Nvidia GTX Titan Z GPU leverages 5760 CUDA cores 2 , while a similar trend can be observed also for embedded computing platforms, see for example the Nvidia Tegra 4 3 or the Adapteva's Parallela board 4 . The second paradigm -what we called heterogeneous parallelism -consists of integrating on the same platform different types of accelerators, to provide better energy efficiency and higher computational throughput to the application developer.…”

Section: Introductionmentioning

confidence: 81%

“…Similarly to the Heartbeat framework [4], our AS-RTM uses high-level monitors of the performance (such as a throughput monitor, a QoS monitor or a user-defined monitor) to sense the execution context and to react to any change in the application requirements. The AS-RTM integration in a third-party application is straightforward: it does not require refactoring the application logic to meet an execution template (such as in [19]) but only wrapping the profiled code with the monitor calls.…”

Section: Application-specific Rtm (As-rtm)mentioning

confidence: 99%

See 1 more Smart Citation

OpenCL Application Auto-tuning and Run-Time Resource Management for Multi-core Platforms

Gadioli

Libutti

Massari

et al. 2014

2014 IEEE International Symposium on Parallel and Distributed Processing With Applications

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To support adaptivity of data parallel applications on multi-core platforms, we propose a framework based on the combination of OpenCL application auto-tuning and runtime resource management. The framework addresses computationally intensive multimedia OpenCL applications. For these target applications, we show that application auto-tuning, based on design-time analysis, can become synergistic with run-time resource management. In the proposed framework, run-time decisions are taken by each application, autonomously, to achieve system adaptivity. This paper describes the methodology and related toolchain, defined during the 2PARMA European project, based on the integration of independent tools to provide effective compilation of OpenCL code, multi-objective design space exploration, application monitoring and tuning and system-wide runtime resource management. Experimental results are reported for design optimization of an OpenCL stereo-matching application and then for a resource contention scenario where multiple stereomatching applications are executed on the same platform with different run-time requirements.

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Section: Introductionmentioning

confidence: 81%

Section: Application-specific Rtm (As-rtm)mentioning

confidence: 99%

OpenCL Application Auto-tuning and Run-Time Resource Management for Multi-core Platforms

Gadioli

Libutti

Massari

et al. 2014

2014 IEEE International Symposium on Parallel and Distributed Processing With Applications

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“…Hoffman et al [15] have developed an interface for diverse applications to report a performance measure in a generic way so that operating systems and runtimes can adapt themselves to optimize application performance. In their Application Heartbeats framework, applications signal a "heartbeat" as they make progress in a computation; for example, a video-encoding application could signal a heartbeat each time it processes a frame.…”

Section: Related Workmentioning

confidence: 99%

An Autonomic Performance Environment for Exascale

Huck

Porterfield²,

Chaimov

et al. 2015

JSFI

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Exascale systems will require new approaches to performance observation, analysis, and runtime decision-making to optimize for performance and efficiency. The standard "first-person" model, in which multiple operating system processes and threads observe themselves and record first-person performance profiles or traces for offline analysis, is not adequate to observe and capture interactions at shared resources in highly concurrent, dynamic systems. Further, it does not support mechanisms for runtime adaptation. Our approach, called APEX (Autonomic Performance Environment for eXascale), provides mechanisms for sharing information among the layers of the software stack, including hardware, operating and runtime systems, and application code, both new and legacy. The performance measurement components share information across layers, merging first-person data sets with information collected by third-person tools observing shared hardware and software states at node-and global-levels. Critically, APEX provides a policy engine designed to guide runtime adaptation mechanisms to make algorithmic changes, re-allocate resources, or change scheduling rules when appropriate conditions occur.

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“…AutoPro requires each SLO-bound VM to make periodic performance reports available to its controller, in order to leverage its resource-performance models, as proposed in previous works [Zhang et al 2002;Padala et al 2009;Shen et al 2011;Sironi et al 2012;Bartolini et al 2013a;Hoffmann et al 2013;Sironi et al 2014]. Any performance metric meaningful to the user can be used for these reports and to express SLOs; for instance, a web server can use throughput (e.g., requests/s for a web server) or latency (i.e., response time).…”

Section: Performance Metrics and Measurementsmentioning

confidence: 99%

“…ControlWare [Zhang et al 2002], METE [Sharifi et al 2011], and PTRADE [Hoffmann et al 2013] served as inspiration for subsystems of AutoPro. These systems tackle environments that differ from virtualization infrastructures, making a full-system comparison with AutoPro unfeasible; instead, we outline quantitative comparisons between subsystems.…”

Section: Related Workmentioning

confidence: 99%

Automated Fine-Grained CPU Provisioning for Virtual Machines

Bartolini

Sironi

Sciuto

et al. 2014

ACM Trans. Archit. Code Optim.

Self Cite

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Ideally, the pay-as-you-go model of Infrastructure as a Service (IaaS) clouds should enable users to rent just enough resources (e.g., CPU or memory bandwidth) to fulfill their service level objectives (SLOs). Achieving this goal is hard on current IaaS offers, which require users to explicitly specify the amount of resources to reserve; this requirement is nontrivial for users, because estimating the amount of resources needed to attain application-level SLOs is often complex, especially when resources are virtualized and the service provider colocates virtual machines (VMs) on host nodes. For this reason, users who deploy VMs subject to SLOs are usually prone to overprovisioning resources, thus resulting in inflated business costs.This article tackles this issue with AutoPro: a runtime system that enhances IaaS clouds with automated and fine-grained resource provisioning based on performance SLOs. Our main contribution with AutoPro is filling the gap between application-level performance SLOs and allocation of a contended resource, without requiring explicit reservations from users. In this article, we focus on CPU bandwidth allocation to throughput-driven, compute-intensive multithreaded applications colocated on a multicore processor; we show that a theoretically sound, yet simple, control strategy can enable automated fine-grained allocation of this contended resource, without the need for offline profiling. Additionally, AutoPro helps service providers optimize infrastructure utilization by provisioning idle resources to best-effort workloads, so as to maximize node-level utilization.Our extensive experimental evaluation confirms that AutoPro is able to automatically determine and enforce allocations to meet performance SLOs while maximizing node-level utilization by supporting batch workloads on a best-effort basis.

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Application heartbeats

Cited by 131 publications

References 20 publications

OpenCL Application Auto-tuning and Run-Time Resource Management for Multi-core Platforms

OpenCL Application Auto-tuning and Run-Time Resource Management for Multi-core Platforms

An Autonomic Performance Environment for Exascale

Automated Fine-Grained CPU Provisioning for Virtual Machines

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