International Conference on Computational Science, ICCS 2010
CyberInfrastructures of Cyber-Applications-Systems
Frederica Darema*
NSF
Arlington VA, 22230
Abstract
The paper discusses foundational notions and conditions in complex applications modeling and in software frameworks to support advanced CyberInfrastructure environments such as those implied and required for Dynamic Data Driven Applications Systems, as well as other classes of applications, collectively referred to here as Cyber-Applications-Systems. The paper discusses considerations of dynamic invocation of multi-scale models, uncertainty quantification, uncertainty propagation, and dynamic runtime systems support on heterogeneous, distributed, end-to-end dynamically integrated high-end, real-time and instrumentation and control systems.
c 2010Publishe
d by Elsevier Ltd.
Keywords: dynamic data driven applications systems; DDDAS; InfoSymbiotic Systems; InfoSymbiotics; multi-scale, multi-modal, multi-data; dynamic multi-scale modeling; uncertainty quantification; uncertainty propagation; cyberinfrastructure; software architectural frameworks; real-time; high-end; instrumentation; control
1.Introduction
Dynamic Data Driven Applications Systems (DDDAS [1]) is an important class of Cyber-Applications-Systems [2][i]. The paper discusses the scope of foundational notions and conditions in complex applications modeling approaches and in implementations and development of software frameworks to support advanced CyberInfrastructure environments, such as those implied and required for emerging classes of applications systems such as Dynamic Data Driven Applications Systems, as well as other classes of complex applications, collectively referred to here as Cyber-Applications-Systems. The DDDAS concept, entailing dynamic integration of computational with instrumentation and control aspects of an application system, implies capabilities that go beyond the standard requirements and considerations in other classes of complex applications such as traditional multi-scale modeling approaches. DDDAS implies dynamic requirements at the application system level as well as dynamic underlying resource management, the later supported through comprehensive software frameworks, referred to here as Cyber-Systems-Software [ii]. In this paper we introduce the term: InfoSymbiotic Systems (and InfoSymbiotics), to denote systems such as DDDAS, and their capabilities and requirements. The paper discusses considerations of dynamic invocation of multi-scale models, uncertainty quantification and uncertainty propagation in such dynamic applications systems, and dynamic runtime systems support on heterogeneous, distributed, end-to-end dynamically integrated high-end, real-time and instrumentation and control systems.
We have used the term Cyber-Applications-Systems to refer to complex applications systems together with their support environments, for applications representing natural or physical systems, engineered systems, and other 1877-0509c 2010Published by Elsevier Ltd.
doi:10.1016/j.procs.2010.04.143
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complex man-made systems, such as civil infrastructure systems, business-process models or social-systems models, which complex computational and data support requirements. The new and advanced capabilities discussed here are aimed to address challenges inherent in such systems, namely: complex applications modeling such as multi-scale, multi-modal, multilevel and multiphase modeling, as well as multi-data aspects (multi-*[iii]); applications systems of systems modeling and in dynamic data driven applications systems (DDDAS, SBES[3], and other challenge areas, such as those discussed in the 14 NAE Grand Challenges[4]); addressing the computational and the data requirements of such applications, and ascertaining fidelity of models through uncertainty quantification, validation and verification of models and their associated software programs, and application analytics. As the complexity of applications and that of their support environments increases, other aspects are relevant in supporting these applications and their environments; these include: application composition, application software evolution, maintenance, stewardship, creation of repositories of application models and their associated algorithms, and application programming environments and runtime support.
Together with Cyber-Applications-Systems, Cyber-Systems-Software [iv] represent advanced, end-to-end systems software enabling: Software Infrastructure Frameworks encompassing programming and runtime support environments for Cyber-Applications-Systems, including situations where the applications need to utilize in an optimized way heterogeneous and distributed resources and achieve quality-of-service, as well as conditions where the underlying resources and the applications requirements can change dynamically, as in the case of DDDAS environments.
We refer the reader to [Ref. 1] for more detailed discussion of the infrastructure and frameworks for systems hardware and systems software design, runtime and maintenance planning, and also support development of applications, application portability across platforms and optimized runtime, and transformative transition of such applic DWLRQV WR WKH 3H[DVFDOH′ GRPDLQ; and Compiler Infrastructures for developing advanced compiler systems, such as those enabling dynamic and adaptive mapping of applications under dynamic underlying resources and dynamically changing requirements of applications at runtime.
2.Application Systems of Systems
https://www.doczj.com/doc/b38791232.html,plex, Multi-* Applications
Mathematical and software representations of complex systems entail multiple interoperating components, capable of representing the multiple characteristics and behaviors of such systems, and dynamically extensible and adaptable to either the complex systems behaviors and their changes or changes in their supporting computational (hardware and software) environments. Representing complex systems such as: natural/physical, engineering systems, including computer software and hardware systems, business systems, business process modeling, social systems, etc), often requires ability to represent their various modalities and aspects, including their behaviors and properties as affected by dynamically changing conditions, some of them hard or impossible to specify and predict a-priori.
Such considerations point to representations of complex application systems which consist of collections of models (multi-modal, multi-phase, multi-level, multi-scale in space and time collectively referred to here as multi-*), and which draw their data from multiple sources, instrumentation measurements, other executing models, which need to be architected together in a systems of systems (of applications models).
A number of Workshops, TaskForces, Blue-Ribbon Panels, and other Reports [Refs. 3, 5, 6, 7, 8, and 9] discuss the need, the opportunities and the challenges where understanding observed behaviors and predicting behaviors in a system involves many levels of models and spanning multiple time-scales and/or spatial-scales. Some illustrative examples include: biological systems modeling, ranging from molecular dynamics, to protein folding, to environmental factors affecting the behavior of the dynamics of a biological cell, to an organism, etc., that is: from quantum, to molecular and continuum (or macroscopic) descriptions of systems; e.g. ion hydration, in oil and gas exploration and recovery (discussed in Ref.2) where the scales of models required range from density
F.Darema/Procedia Computer Science1(2010)1287–12961289
Author name / Procedia Computer Science 00 (2010) 000±000 functional theory, to molecular dynamics, to nano- and micro-fluidics and micromechanics, to macro-chemistry of
large molecules and macro-fluidics, to seismic and EM imaging, to geo-mechanics of rock fracturing, and large
scale reservoir modeling; dynamic systems like hurricanes, involve complex atmospheric turbulence models
coupled with oceanic water surge models, and multiple observational monitoring data. In addition to multi-*
needs, such applications can benefit from new computational paradigms, as discussed in the subsection below.
In multi-* modeling, both from the application modeling and application algorithms point of view, one needs to
ensure that appropriate models are included, depending for example for the system configuration that needs to be
modeled, so that the application conceptual or mathematical representation reflects the multiple modalities of the
application system, such as multi-physics, or multiple dimensional- or time-scales, representing various aspects of
the system. In multi-modal, multilevel and/or multi-scale modeling, one needs, for example, to address challenges
of how to link models of disparate time and spatial scales; and understand and maintain algorithmic stability
across the multi-modal models and the differing time and spatial scales. We discuss support for such
compositional capabilities in the Subsection A.4 here, on Application Components and Application Composition
Systems; enabling such capabilities is part of the overall CyberInfrastructure frameworks needed for the
development and runtime support of the complex multi-* applications discussed in the present report and in
[Ref.1].
2.2.New Paradigms and Directions
As has been articulated in the Dynamic Data Driven Applications Systems (DDDAS) concept, DDDAS3entails
the ability to dynamically incorporate additional data into an executing application, and in reverse, the ability of
DQ DSSOLFDWLRQ WR G\QDPLFDOO\ VWHHU WKH PHDVXUHPHQW SURFHVV′[v].The approach results into more accurate
modeling and ability to speed-up the computation (by augmenting or replacing targeted parts of the computation
by the measurement data), thus improving analysis and prediction capabilities of the application model. In
addition, application-driven measurement capabilities in DDDAS enable more efficient and effective
measurement processes. DDDAS environments entail integration across a range of software systems from high
performance computational environments to real-time systems, and a range of hardware and platforms from high-
end platforms to sensors and other instruments and real-WLPH GDWD DFTXLVLWLRQ V\VWHPV WR 33'$V′ DQG 5),'V
DDDAS is a key concept to improving modeling of systems under dynamic conditions, and is a key concept in
architecting and controlling dynamic and heterogeneous resources, including sensor networks, networks of
embedded controllers, and other networked resources.
The transformative advances in computational modeling of applications (an in particular those that represent
dynamic systems) enabled through the DDDAS concept have been articulated in several forums and demonstrated
through advances in a wide set of applications (presented in the ICCS DDDAS Workshop Series [vi] , the DDDAS
program solicitation [Ref. Error! Bookmark not defined.], many articles on DDDAS based-research [vii],
and the SBES Blue-Ribbon Report [Ref. 3]. These references discuss extensively that to enable such capabilities
requires multidisciplinary research, and specifically the need for synergistic and systematic collaborations
between applications domain researchers with researchers in mathematics and statistics, researchers computer
sciences, and researchers involved in the design and implementation of measurement and control (methods,
instruments, and other sensors and other control systems). Enabling and supporting DDDAS capabilities entails
modeling methodologies and runtime support that go beyond present approaches. For example, dynamically
integrating additional and external data, streamed into the executing application, to replace or complement part of
the computation, entails and requires for example the ability of the application algorithms (numeric and non-
numeric) to be tolerant, and maintain good convergence properties under perturbations from the streamed data.
The dynamically incorporated data into the executing application can cause the need to invoke dynamically
models representing other scales, behaviors and modalities of the application. This implies not only multi-scale
modeling capabilities (discussed in the next sub-section), but dynamic invocation of these models of differing
scales and/or differing modalities. Furthermore such dynamic computational requirements imply ability to
dynamically acquire underlying support resources and dynamically and optimally map these application
components into such resources, and support the ensuing runtime conditions on heterogeneous and distributed
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computational, communication, and data-support environments. Likewise in DDDAS environments, uncertainty quantification and uncertainty propagation (discussed in a subsequent subsection) is not only concerning a single model but how uncertainties propagate across dynamically invoked models and how uncertainties in the streamed data can be quantified.
In addition, there are a number of large instrumentation cyberinfrastructures efforts that have been initiated (e.g. those by NSF and cited in Ref. 1) which can benefit from the DDDAS concept. These include newly established instrumentation cyberinfrastructures, as well as a number of earlier efforts as they evolve into the future, enabled through advances in high-end computing, grid computing, and now also cloud computing, sensor systems, etc. Examples include targeted cyberinfrastructure-enabling projects such as: the Network for Earthquake Engineering Simulation (NEES); the Open Science Grid (which includes earlier HIIRUWV VXFK DV WKH 3LQWHUQDWLRQDO 9LUWXDO 'DWD Grid Laboratory ±L9'*/′ WKH /DUJH +DGURQ &ROOLGHU /+& WKH &KHPLVWU\ DQG 0DWHULDOV &RQVRUWLXP IRU Advanced Radiation Sources (ChemMatCARS); Energy Recovery Linac (ERL) at CHESS; the Vibrational Spectrometer for Spallation Neutron Source (SNS) at ORNL (VISION); Data Acquisition For Neutron Scattering Experiments (DANSE), and Center for High Resolution Neutron Scattering (CHRNS); the National Ecological Observatory Network (NEON) and the Federation of Environmental Networks (FEON); and the Geosciences Network (GEON); all these and more that may be pursued in the future, provide excellent examples of applications where DDDAS can be used to enhance research productivity and the impact of simulation and measurements enabled by these infrastructure projects.
To exemplify the CyberInfrastructure frameworks needed for the development and runtime support of the DDDAS applications and their environments here are summarized some of the research and technology development challenges and opportunities [Refs 1 and 2]. In DDDAS implementations, an application/simulation must be able to accept data at execution time and be dynamically steered by such dynamic data inputs. This requires research advances in application models that: describe the application system at different levels of detail and modalities; are able to dynamically invoke appropriate models as needed by the dynamically injected data into the application; and include interfaces of applications to measurements and other data systems. Application Measurement Systems and Methods include improvements and innovations in instrumentation platforms, and improvements in the means and methods for collecting data, focusing in a region of relevant measurements, controlling sampling rates, multiplexing, and determining the architecture of networked sensor assemblies and other measurement systems. Advances in Mathematical and Statistical Algorithms include creating algorithms with stable and robust convergence properties under perturbations induced by dynamic data inputs: algorithmic stability under dynamic data injection/streaming; algorithmic tolerance to data perturbations; multiple scales and model reduction; enhanced asynchronous algorithms with stable convergence properties. Advances in Systems Software runtime support and infrastructures to support the execution of applications whose computational systems resource requirements are dynamically dependent on dynamic data inputs, and include: dynamic selection at runtime of application components embodying algorithms suitable for the kinds of solution approaches depending on the streamed data, and depending on the underlying resources, dynamic workflow driven systems, coupling domain specific workflow for interoperation with computational software, general execution workflow, software engineering techniques. Software Infrastructures and other systems software (OS, data-management V\VWHPV DQG RWKHU PLGGOHZDUH VHUYLFHV WR DGGUHVV WKH 3UHDO WLPH′ FRXSOLQJ RI GDWD DQG computations across a wide area heterogeneous dynamic resources and associated adaptations while ensuring application correctness and consistency, and satisfying time and policy constraints. Specific features include the ability to process large volume, high rate data from different sources including sensor systems, archives, other computations, instruments, etc.; interfaces to physical devices (including sensor systems and actuators), and dynamic data management requirements. Also support is needed for accessing and visualizing at runtime, computational, measured and sensed data. In addition the new environments considered here require integrated frameworks for accessing grid resources that support research exploration, workflow capture and replay, and a dynamic services oriented architecture, with standards protocols. All these, and also as articulated in [Refs 1 and 2], also need to be integrated in into comprehensive software frameworks to support DDDAS CyberInfrastructures, and into Community Testbeds. A number of DDDAS research efforts have started developing cyberinfrastructure software frameworks; such efforts can evolve to robust prototype versions and tools for a broader set of efforts.
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Author name / Procedia Computer Science 00 (2010) 000±000 In the next Section are discussed other important considerations in computational modeling, namely ascertaining
validity and fidelity of the models, by understanding and addressing the impact of uncertainties in models and
data, validation and verification of models, etc. In DDDAS environments these challenges, of modeling
uncertainty, propagation of uncertainty, etc, they are further augmented as the application itself changes
dynamically at execution time. DDDAS environments spur the need for advances in these areas.
3.Uncertainty, Validation and Verification, Application Analytics, and Application Driven Analytics
One of the most important tenets of modeling is the ability to analyze systems to understand the observed and
predict the yet unobserved. In other words one of the objectives of PRGHOLQJ LV WR EH D 3SUHGLFWLYH VFLHQFH′ 7R
fulfill such goals, assessing the fidelity and accuracy of application models is very important. This is necessary, not
only to be able to accurately analyze and understand the modeled system, but also to be able to make accurate
predictions of not yet observed characteristics, properties, and behaviors of a system, and to discover new
phenomena in natural and physical systems, predict the range of capabilities and predict new capabilities in
engineered systems, predict the systems behaviors under these new capabilities, and analyze such behaviors in hard
to test or in rare extreme situations. Understanding the range of applicability of a model is thus very important for
achieving such objectives.
3.1.Uncertainty, Validation and Verification
The fidelity of a model is affected considerably not only by the accuracy by which the model represents a system,
but also by the uncertainty in the data that are used to drive the model, and how these uncertainties propagate as
the simulation proceeds. Much work has been devoted in evaluating the impact of these sources of uncertainty as
well as uncertainty propagation, including faster methods for computing the propagation of the uncertainty
bounds. There are many methods of calculating uncertainty, including faster polynomial based methods. Also
related to that is estimation of the fidelity of computation in cases of faulty or incomplete data [viii]. Given the
multi-* systems and other emerging dynamic systems (like DDDAS) discussed here, efforts in estimation of
uncertainties remain important areas.
Validation is the process of determining the accuracy by which a model (or the collection of models in the case of
multi-modal, multi-level, multi-scale modeling) represents the actual system, the accuracy by which it can
characterize and describe the actual system, and/or the accuracy by which the model can predict behaviors of a
system. Verification is the accuracy by which a computational representation (computer program) of the
mathematical model implements the mathematical model. Validation asks: are the right equations used and
solved mathematically? Verification asks: does the computer program solve these equations correctly? [note: this
is a somewhat different definition that the one given in Ref. 3].
Verification of the model (that is of the computer program) involves software engineering methods, for testing
and ensuring correctness of execution, and detection of various programming and execution errors (program
bugs). While software engineering methods have been effective in small and custom designed applications, for
the large, complex, and compute- and data-intensive applications of interest here, software engineering methods
are rather slow and inadequate to provide verification for entire programs
With current approaches, for large applications models, both validation and verification of models and their
associated computer programs, is done by checking the fidelity of the model (and its associated computer
program) against some predetermined expectations either dictated by data from actual instantiations of the system.
Thus, validation of the model entails checking its ability against points or regions of the solution space. Typically
LV QRW SRVVLEOH WR FRYHU DOO WKH VROXWLRQ VSDFH WKH PRGHOV DUH YDOLGDWHG DJDLQVW 3LPSRUWDQW′ WHVW FDVHV UDWKHU WKDQ
exhaustively; in fact, typically it is a small subset of the solution space of the problem.
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The opportunities for research here are in developing more comprehensive approaches of verification and validation not only of individual models, but also of the complex collections of models, such as those that are encountered in multi-modal and multi-scale applications modeling (hat is in application systems of systems). Similar challenges and opportunities concern effects of uncertainties in modeling and the fidelity of results, as discussed earlier in reference to multi-*.
3.2.Application Analytics and Application Driven Analytics
Related to considerations about multi-scale, multi-level, multi-modal modeling, uncertainty, validation and verification, are the technology and research areas relating to Application Analytics, and Application Driven Analytics. Some key challenges and opportunities are discussed next.
Application analytics is the study allowing to understand the range of validity of the application models (the individual models and complex assemblies of such models, as in the case of multi-*). Application analytics includes quantification of the quality of input application data, their uncertainties, or inconsistent and incomplete data, and understanding the range and coverage of tests in the validation of the application models and the coverage of testing in the verification of the associated application software.
In a related fashion, we consider in this report application analytics as connected to Application Driven Analytics of the actual system represented by the application. In this context the application model is used to understand a range of behaviors and characteristics of the system, and also the bounds of such behaviors and characteristics; for example understanding the range of stability of an engineered system under certain conditions and how a (complex) system will behave when perturbed beyond its design points. Fidelity of such application-driven analytics of the system depends on the understanding of the range of validity of the application model(s) involved (as defined and assessed through application analytics).
Another perspective of applying analytics is in Application Driven Analytics of computer systems. An approach IRU WHVWLQJ WKH FDSDELOLWLHV RI V\VWHPV? VRIWZDUH DQG RI WKH underlying hardware platforms is to create test cases driven by known applications (whose models have been validated, and their respective application software has been verified). Through this approach one can create a sampling of scenarios and quantification of quality attributes of the characteristics and behavior of the said systems software and the hardware platforms. Benchmarking is a case example of application-driven analytics that allows characterizing the capabilities of a software or hardware system driven from a range of selected applications (benchmarking set). Often it is desirable to test, through such benchmarking methods, the capabilities of entire computational environment stack, provided by the hardware platforms together with the supporting systems software. As discuss in [Ref. 1] a methodology to perform such an analysis of software and hardware requires systematic approaches, such as modeling and analysis software frameworks encompassing multi-level, multi-modal, multi-scale application software, systems software and hardware models.
4.Application Components and Application Composition Systems
From the application software and application execution point of view we need to address aspects of the requirements and capabilities for composing such applications, the need for assists and tools that can enable the composition of the application at the development time (for example knowledge based recommender systems). Other aspects to be considered in the application composition are for example application models and algorithms appropriate for the data sets considered and the characteristics of the underlying platforms on which the application will execute.
In these cases, as we discuss later-on the CyberSystemsSoftware section, one also needs to consider the ability of application composition dynamically at execution time, in the cases where the underlying resources change and/or the requirements of the application change as the application executes. Approaches discussed there are high-level programming tools (e.g. visual programming or script±driven high-level user interfaces for user directed assists), as
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Author name / Procedia Computer Science 00 (2010) 000±000 well as advanced problem solving environments, recommender systems, using archival high-level descriptions of
characteristics of such components, their data, and the underlying platforms architectural characteristics, and
knowledge based approaches, to enable optimized composition of applications, at application development stage
and also dynamic composition at execution time, in response to changing underlying resources and/or changing
requirements of the application (for example in executing models of multiple scales).
Furthermore, in the case of DDDAS environments, scientific and engineering simulations of complex physical
phenomena, entail combining dynamically (and opportunistically, as needed) computations, instrumentation
measurements (archival and real-time data), and frequently involve dynamic invocation of application models and
algorithms. For example in a given application (representing a physical, engineering, biological, economic system)
models of different levels or modalities may be dynamically invoked, as dictated by the dynamic data inputs, and the
ability to dynamically compose and couple different components is a common theme in DDDAS applications.
Another related common theme is the need to support dynamic assimilation of data and from varying numbers and
classes of sensors. The need for and ability to dynamically compose simulation components, to support dynamic
choice between different simulation models and on-demand real-time sensor data assimilation requirements pose
substantial systems software challenges. Approaches to dynamic composition are likely to involve metadata
management schemes, development of schemes for supporting semantic functional and performance oriented data
service interfaces along with workflow scheduling and management schemes.
The challenges here relate and cross-over the issues discussed in the section on uncertainty quantification, model
verification and validation. The discussion of the challenges there applies not only to the individual models, but also
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5.Application Software Evolution and Maintenance
Software for applications modeling evolves, because of several broad factors, such as: changes in the application
modeling approaches, changes in application software and the support software environments, and changes in the
hardware platforms:
changes in the application modeling approaches include: improved understanding of the theoretical principles or the fundamental concepts about the system, leading to improvements in the application models, new modeling
paradigms, new modeling methods and advances in the fundamental algorithms involved in the modeling, need
to meet new analysis requirements, need to incorporate additional factors (such as multi-modal, multilevel,
multi-VFDOH FDSDELOLWLHV FRXSOH WKH JLYHQ PRGHO V ZLWK RWKHU DSSOLFDWLRQV? P odels (for example: in fire
modeling, couple radiative-heat transfer models with air turbulence models), interface models with additional
data sources (differing data models);
changes in application software, include correcting errors in the application program and other software faults such as software performance and reliability deficiencies; adapt the application software to operate in new
execution environments, like new programming paradigms, hardware platforms, runtime systems, operating
systems, data management and I/O systems; and overall modifying the application software architecture,
rewriting parts of the application to improve software structure, to make the application software more efficient
and maintainable. {e.g. evolving the software fr DPHZRUN? `
changes in the support software environments (like OS, compilers, I/O, DBMS systems, etc) and of course changes in the hardware architecture (processing node architecture, interconnects architecture, size of memory
in any of the memory hierarchies, peripherals, or other hardware resources), all these they often precipitate
needs to change (or evolve) the application software, especially when one wants also the application to execute
with optimized performance and QoS.
All these challenges argue for more systematic methods for fundamental computer sciences research and synergistic
collaboration between computer-science researchers and computational scientists to develop software engineering
approaches and tools, driven-by and accommodating the needs of large, complex, and dynamic applications
referenced here.
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The open software approach is an important and relatively recent approach, which does have impact in approaches on software evolution and maintenance. Of course open software has other broader impacts (such allowing multiple stakeholders to use the software developed), but here we discuss open software in the context of evolution and maintenance, and as complementary to other approaches addressing the objectives discussed above and in reference to its role for addressing gaps in these efforts. For example, by making the software available to the broader community, an application model, or an application component, or an algorithm, not only the contribution is used and leveraged by others, but also by having more users deploy this software, then it can be tested more extensively, thus providing more comprehensive testing, and validation and verification of the application models and their associated software.
In summary, it is important to provide support of the full software life-cycle in the ecosystem of people, software, data and hardware, and in the context of software frameworks spanning the application, the systems software and application services layers and bridging to the hardware layers.
6.Repositories of Applications Models
The increasing complexity of applications and the associated software has motivated for sometime now the need to create libraries of application models and algorithms. It is the desire to leverage rather than duplicate work in application modeling and algorithmic research, and in software development. Examples there-of include numerical libraries (like LINPACK, LAPACK, EISPACK, etc), software PETC, and numerous application packages (e.g. GAUSSIAN, GAMESS, AMBER, CHARMM, NAMD, GROMOS, LAMMPS, etc, just to name some packages in computational chemistry area and molecular dynamics methods; many other science and engineering areas also have corresponding applications packages, the list being too long to include all here).
In addition, several efforts have aimed in the past at creating problem solving environments where knowledge based V\VWHPV FDQ 3UHFRPPHQG′ DSSURSULDWH PRGHOV DOJRULWKPV DQG RWKHU VRIWZDUH FRPSRQHQWV H J 36(V WKH 3UHFRPPHQGHU V\VWHP′ SURMHFWV [10]). While these efforts have provided considerable help to application developers, they have not reached the level of capabilities that are needed for the kinds of advanced dynamic application systems and environments that are encountered today and envisioned in the future. Such present and IXWXUH DSSOLFDWLRQ V\VWHPV UHTXLUH XVH RI 3knowledge-based UHFRPPHQGHU′ V\VWHPV DV IRVWHUHG E\ WKH 1*6 program) to automate the mapping and optimized runtime of applications (as discussed in more detail in the systems software section below), and provide the support that is envisioned as needed by emerging application environments (such as the DDDAS discussed earlier-RQ $OVR VXFK 3NQRZOHGJH-EDVHG′ UHFRPPHQGHU V\VWHPV DUH QHHGHG WR support dynamic Application Composition Systems capabilities (ideas discussed earlier-on in this report, and fostered with the NGS and DDDAS Programs, and also discussed in the CyberSystemsSoftware section below).
In addition to application models, one needs the repositories to include information on the range of validity of the models, both in terms of the application cases that such models may apply, and also in terms of the data sets and data sizes that the models are suitable, the underlying platforms and data-sets to test the models, and ability to include new data sets in the panoply of model testing. Again knowledge-based approaches are also applicable here.
Furthermore, the kinds of model repositories discussed here will need to include data sets for validation of the models and verification of their associated software, and also capture information over time, as the models evolve and as their associated software methods evolve, and also as the underlying hardware platforms evolve. For example, one needs to c apture not only dat D DQG PHWDGDWD DERXW WKH GDWD EXW DOVR LQFOXGH WKH LQSXW RI GHYHORSHUV? and other experts who have been involved in the development of the models and the software (their comments, remarks and other information).
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Author name / Procedia Computer Science 00 (2010) 000±000 7.Application Programming Environments
Modern applications consist of multiple inter-operating compute- and data-intensive components. To obtain
accurate models and simulations, or to deliver real-time results, the applications need to execute on high-
performance globally distributed and high-end petaflops-classes (and now hexaflops- and beyond) computing
platforms. At the same time these applications need to achieve high-efficiency and QoS when executing on such
platforms.
The present methods of building applications result in applications that are designed for a given platform. When the
underlying platform changes often significant parts of the application need to be rewritten for the new platform(s).
This is costly and limiting: the resulting applications cannot automatically move to the new platform; the
applications cannot be distributed to run concurrently on the old and the new platforms; the applications cannot be
dynamically partitioned across globally distributed platform assemblies, map dynamically across such platforms as
the resource availability changes and exploit such platform assemblies with quality-of-service. Similar obstacles
exist when the problem size changes and the application needs, for example, to be repartitioned and remapped for
the bigger problem size. Today's technology mostly relies on considerable and laborious hand tuning. Over the last
decade, efforts to enable more effective use of distributed computing platforms have been launched. These efforts
articulated the need for automating the process of distributing and mapping the application across such platforms, as
well as optimizing the mapping of the application on a given high-end platform (e.g.: NGS 1998-2004 [11]; IBM
Autonomic Computing Initiative in 2001 [12]).
The advent of multi-core based platforms, will accentuate the referenced problems, to even higher levels of
complexity, and exascale architectures and exascale-class applications augment the complexity of these kinds of
requirements and challenges. Furthermore, applications environments like DDDAS they also pose a number of
unique system software and support requirements. These kinds of applications involve an element of adaptive real
time response (hard- or soft-, or both, depending on the control task or application phase), that is, they require
seamlessly integrated environments, from 3WKH UHDO-time to the high-HQG′ 'LVFXVV ion of computational models and
runtime support for DDDAS environments are provided in [13, 14, and Ref. 2]. Systems software (also referred to as
middleware [ix]) used to support DDDAS therefore needs to be able to address aspects of performance prediction,
performance negotiation, and performance guarantees. Furthermore, DDDAS environments require system level
quality of service guarantees. DDDAS applications typically require multiple computational inputs within
sometimes predictable, and sometimes unpredictable, periods of time. The timescales differ from application to
application, and possibly for different stages and tasks of a given application. For example, some applications may
require millisecond level responses while others require a range of responses in seconds, hours, days or weeks
depending on the nature of the control task. In all of these cases, DDDAS software stacks need to be able to support
predictable temporal response to such varying characteristics. In such environments it becomes imperative to
augment and more systematically support efforts such as in [Ref. 2].
8.Summary
This paper has addressed has addressed considerations in advanced complex applications modelling, such as
multi-scale modelling and uncertainty quantification, and in particular in the context of dynamically integrated
computational, instrumentation and control systems, such as in DDDAS, and in the context of creating
cyberinfrastructures supporting such environments as encompassed in Cyber-Applications-Systems and Cyber-
Systems-Software frameworks entailed in these environments.
References
1. DDDAS: https://www.doczj.com/doc/b38791232.html,/dddas and https://www.doczj.com/doc/b38791232.html,
1296F.Darema/Procedia Computer Science1(2010)1287–1296
Author name / Procedia Computer Science 00 (2010) 000±000
2. F. Darema, Monograph submitted for publication by Springer; the term Cyber-Applications-Systems introduced ibid; and prior to that in
the NSF internal reports: F. Darema: Report on New Opportunities for OCI: CyberIfrastructures of Cyber-Applications-Systems & Cyber-Systems-Software, October 2009, and F. Darema Report on Industrial Partnerships in Cyberinfrastructure (3,QQRYDWL on through &\EHU,QIUDVWUXFWXUH ([FHOOHQFH′- ICIE)
3. SBES-Oden: https://www.doczj.com/doc/b38791232.html,/pubs/reports/sbes_final_report.pdf
4. NAE: https://www.doczj.com/doc/b38791232.html,
5. Frederica Darema-Transformative Research Report [March 2008]; included in Ref.1
6. https://www.doczj.com/doc/b38791232.html,/mediawiki/images/a/a7/Messina-doeexa.pdf - Workshops Series (organized by Paul Messina): Climate, November
6-7, 2008; HEP, December 9-11, 2008; Nuclear Physics, January 26-28, 2009; Fusion Energy Sciences, March 18-20, 2009; Nuclear Energy, May 11-12, 2009; BES, August 13-15, 2009; Biology, August 17-19, 2009; NNSA, October 6-8, 2009
7. https://www.doczj.com/doc/b38791232.html,/workshops/town_hall/reports/mod_sim.pdf; Modeling at the exascale for Energy and the Environment (Horst
Simon, Thomas Zacharia and Rick Stevens)
8. https://www.doczj.com/doc/b38791232.html, ± IEST: International Exascale Software Project (led by Jack Dongarra) with a series of workshops starting at SC08,
Nov 18, 2008, and with three recent ones in Santa Fe, NM USA, April 7-8, 2008; Paris, France, June 28-29, 2009; Tsukuba, Japan, October 19-21, 2009
9. https://www.doczj.com/doc/b38791232.html,/sbes/ and SBES-Global: https://www.doczj.com/doc/b38791232.html,/sbes/SBES-GlobalFinalReport.pdf - International Assessment of
Research and Development in
Simulation-based Engineering and Science
10. Rice-Houstis-Ramakrishnan, etal fir VW 3UHFRPPHQGHU SURMHFW′ LQ DQG 3<7+,$-II (2000), and follow-on work by Ramakrishnan;
and POEMS project (NGS) James Browne et.al. (2000)
11. NGS: https://www.doczj.com/doc/b38791232.html,/pubs/2001/nsf01147/nsf01147.htm (and previous 1998 - )
12. https://www.doczj.com/doc/b38791232.html,/autonomic
13. Frederica Darema, Characterizing Dynamic Data Driven Applications Systems (DDDAS) in Terms of a Computational Model, Lecture
Notes in Computer Science (LNCS), Volume 5545/2009, May 2009; and Journal of Algorithms and Computational Technology (JACT)
14. F. Darema, Grid Computing and Beyond: The Context of Dynamic Data Driven. Applications Systems, (Invited Paper),Proceedings of
the IEEE, Special Issue on Grid Computing, Edited by M. Parashar and C. A. Lee, Vol. 3, No. 3, March 2005
i Term introduced by the author in Ref.2
ii Term introduced by the author in Ref.2
iii3PXOWL- ′ GHQRWHV 3PXOWL-PRGHO′ DQG 3PXOWL-GDWD′ DSSOLFDWLRQV WKH WHUP 3PXOWL-PRGHO′ LV XVHG LQ WKLV report as encompassing: multi-modal, multi-phase, multi-level, multi-scale (also refer to subsequent sections and discussions in Ref.1)
iv Term introduced by the author in Ref.2
v3'''$6 LV D SDUDGLJP ZKHUHE\ DSSOLFDWLRQ VLPXODWLRQV DQG WKHLU PHDVXUHPHQWV become a symbiotic feedback control system, enabling applications that adapt intelligently to evolving conditions, and that infer new knowledge in ways that are not predetermined by startup SDUDPHWHUV ′ In addition through the notion of dynamic control of measurement processes dictated by the application at runtime, entails capabilities for dynamic and optimized management of heterogeneous measurement and other resources.
vi DDDAS Workshops: https://www.doczj.com/doc/b38791232.html,/cise/cns/dddas/2006_Workshop/index.jsp and
https://www.doczj.com/doc/b38791232.html,/cise/cns/dddas/index_wkshp.jsp
vii DDDAS Workshop Series at the International Conference on Computational Sciences (yearly 2003- ?
viii See also Oil&Gas Exploration&Production application example in [Ref.1]
ix Although in principle middleware (which originally started as software for the networking layer) is a subset of Systems Software
如何写先进个人事迹 篇一：如何写先进事迹材料 如何写先进事迹材料 一般有两种情况：一是先进个人，如先进工作者、优秀党员、劳动模范等；一是先进集体或先进单位，如先进党支部、先进车间或科室，抗洪抢险先进集体等。无论是先进个人还是先进集体，他们的先进事迹，内容各不相同，因此要整理材料，不可能固定一个模式。一般来说，可大体从以下方面进行整理。 (1)要拟定恰当的标题。先进事迹材料的标题，有两部分内容必不可少，一是要写明先进个人姓名和先进集体的名称，使人一眼便看出是哪个人或哪个集体、哪个单位的先进事迹。二是要概括标明先进事迹的主要内容或材料的用途。例如《王鬃同志端正党风的先进事迹》、《关于评选张鬃同志为全国新长征突击手的材料》、《关于评选鬃处党支部为省直机关先进党支部的材料》等。 (2)正文。正文的开头，要写明先进个人的简要情况，包括：姓名、性别、年龄、工作单位、职务、是否党团员等。此外，还要写明有关单位准备授予他(她)什么荣誉称号，或给予哪种形式的奖励。对先进集体、先进单位，要根据其先进事迹的主要内容，寥寥数语即应写明，不须用更多的文字。 然后，要写先进人物或先进集体的主要事迹。这部分内容是全篇材料
的主体，要下功夫写好，关键是要写得既具体，又不繁琐；既概括，又不抽象；既生动形象，又很实在。总之，就是要写得很有说服力，让人一看便可得出够得上先进的结论。比如，写一位端正党风先进人物的事迹材料，就应当着重写这位同志在发扬党的优良传统和作风方面都有哪些突出的先进事迹，在同不正之风作斗争中有哪些突出的表现。又如，写一位搞改革的先进人物的事迹材料，就应当着力写这位同志是从哪些方面进行改革的，已经取得了哪些突出的成果，特别是改革前后的．经济效益或社会效益都有了哪些明显的变化。在写这些先进事迹时，无论是先进个人还是先进集体的，都应选取那些具有代表性的具体事实来说明。必要时还可运用一些数字，以增强先进事迹材料的说服力。 为了使先进事迹的内容眉目清晰、更加条理化，在文字表述上还可分成若干自然段来写，特别是对那些涉及较多方面的先进事迹材料，采取这种写法尤为必要。如果将各方面内容材料都混在一起，是不易写明的。在分段写时，最好在每段之前根据内容标出小标题，或以明确的观点加以概括，使标题或观点与内容浑然一体。 最后，是先进事迹材料的署名。一般说，整理先进个人和先进集体的材料，都是以本级组织或上级组织的名义；是代表组织意见的。因此，材料整理完后，应经有关领导同志审定，以相应一级组织正式署名上报。这类材料不宜以个人名义署名。 写作典型经验材料-般包括以下几部分： (1)标题。有多种写法，通常是把典型经验高度集中地概括出来，一
How to manage time Time treats everyone fairly that we all have 24 hours per day. Some of us are capable to make good use of time while some find it hard to do so. Knowing how to manage them is essential in our life. Take myself as an example. When I was still a senior high student, I was fully occupied with my studies. Therefore, I hardly had spare time to have fun or develop my hobbies. But things were changed after I entered university. I got more free time than ever before. But ironically, I found it difficult to adjust this kind of brand-new school life and there was no such thing called time management on my mind. It was not until the second year that I realized I had wasted my whole year doing nothing. I could have taken up a Spanish course. I could have read ten books about the stories of successful people. I could have applied for a part-time job to earn some working experiences. B ut I didn’t spend my time on any of them. I felt guilty whenever I looked back to the moments that I just sat around doing nothing. It’s said that better late than never. At least I had the consciousness that I should stop wasting my time. Making up my mind is the first step for me to learn to manage my time. Next, I wrote a timetable, setting some targets that I had to finish each day. For instance, on Monday, I must read two pieces of news and review all the lessons that I have learnt on that day. By the way, the daily plan that I made was flexible. If there’s something unexpected that I had to finish first, I would reduce the time for resting or delay my target to the next day. Also, I would try to achieve those targets ahead of time that I planed so that I could reserve some more time to relax or do something out of my plan. At the beginning, it’s kind of difficult to s tick to the plan. But as time went by, having a plan for time in advance became a part of my life. At the same time, I gradually became a well-organized person. Now I’ve grasped the time management skill and I’m able to use my time efficiently.
英语演讲稿：未来的工作 这篇《英语演讲稿范文：未来的工作》，是特地，希望对大家有所帮助！ 热门演讲推荐：竞聘演讲稿 | 国旗下演讲稿 | 英语演讲稿 | 师德师风演讲稿 | 年会主持词 | 领导致辞 everybody good afternoon:. first of all thank the teacher gave me a story in my own future ideal job. everyone has a dream job. my dream is to bee a boss, own a pany. in order to achieve my dreams, i need to find a good job, to accumulate some experience and wealth, it is the necessary things of course, in the school good achievement and rich knowledge is also very important. good achievement and rich experience can let me work to make the right choice, have more opportunities and achievements. at the same time, munication is very important, because it determines whether my pany has a good future development. so i need to exercise their municative ability. i need to use all of the free time to learn
小学生个人读书事迹简介怎么写800字 书,是人类进步的阶梯,苏联作家高尔基的一句话道出了书的重要。书可谓是众多名人的“宠儿”。历来,名人说出关于书的名言数不胜数。今天小编在这给大家整理了小学生个人读书事迹,接下来随着小编一起来看看吧! 小学生个人读书事迹1 “万般皆下品,惟有读书高”、“书中自有颜如玉,书中自有黄金屋”,古往今来,读书的好处为人们所重视,有人“学而优则仕”,有人“满腹经纶”走上“传道授业解惑也”的道路……但是,从长远的角度看,笔者认为读书的好处在于增加了我们做事的成功率,改善了生活的质量。 三国时期的大将吕蒙,行伍出身,不重视文化的学习,行文时,常常要他人捉刀。经过主君孙权的劝导,吕蒙懂得了读书的重要性,从此手不释卷,成为了一代儒将,连东吴的智囊鲁肃都对他“刮目相待”。后来的事实证明,荆州之战的胜利,擒获“武圣”关羽,离不开吕蒙的“运筹帷幄,决胜千里”,而他的韬略离不开平时的读书。由此可见,一个人行事的成功率高低,与他的对读书,对知识的重视程度是密切相关的。 的物理学家牛顿曾近说过,“如果我比别人看得更远,那是因为我站在巨人的肩上”,鲜花和掌声面前,一代伟人没有迷失方向,自始至终对读书保持着热枕。牛顿的话语告诉我们,渊博的知识能让我们站在更高、更理性的角度来看问题,从而少犯错误,少走弯路。
读书的好处是显而易见的,但是,在社会发展日新月异的今天,依然不乏对读书,对知识缺乏认知的人,《今日说法》中我们反复看到农民工没有和用人单位签订劳动合同,最终讨薪无果;屠户不知道往牛肉里掺“巴西疯牛肉”是犯法的;某父母坚持“棍棒底下出孝子”,结果伤害了孩子的身心,也将自己送进了班房……对书本,对知识的零解读让他们付出了惨痛的代价,当他们奔波在讨薪的路上,当他们面对高墙电网时,幸福,从何谈起?高质量的生活,从何谈起? 读书,让我们体会到“锄禾日当午,汗滴禾下土”的艰辛;读书,让我们感知到“四海无闲田,农夫犹饿死”的无奈;读书,让我们感悟到“为报倾城随太守,西北望射天狼”的豪情壮志。 读书的好处在于提高了生活的质量,它填补了我们人生中的空白,让我们不至于在大好的年华里无所事事,从书本中,我们学会提炼出有用的信息,汲取成长所需的营养。所以,我们要认真读书,充分认识到读书对改善生活的重要意义,只有这样,才是一种负责任的生活态度。 小学生个人读书事迹2 所谓读一本好书就是交一个良师益友,但我认为读一本好书就是一次大冒险,大探究。一次体会书的过程,真的很有意思,咯咯的笑声,总是从书香里散发;沉思的目光也总是从书本里透露。是书给了我启示,是书填补了我无聊的夜空,也是书带我遨游整个古今中外。所以人活着就不能没有书,只要爱书你就是一个爱生活的人,只要爱书你就是一个大写的人,只要爱书你就是一个懂得珍惜与否的人。可真所谓
关于坚持的英语演讲稿 Results are not important, but they can persist for many years as a commemoration of. Many years ago, as a result of habits and overeating formed one of obesity, as well as indicators of overall physical disorders, so that affects my work and life. In friends to encourage and supervise, the participated in the team Now considered to have been more than three years, neither the fine rain, regardless of winter heat, a day out with 5:00 time. The beginning, have been discouraged, suffering, and disappointment, but in the end of the urging of friends, to re-get up, stand on the playground. 成绩并不重要，但可以作为坚持多年晨跑的一个纪念。多年前，由于庸懒习惯和暴饮暴食，形成了一身的肥胖，以及体检指标的全盘失常，以致于影响到了我的工作和生活。在好友的鼓励和督促下，参加了晨跑队伍。现在算来，已经三年多了，无论天晴下雨，不管寒冬酷暑，每天五点准时起来出门晨跑。开始时，也曾气馁过、痛苦过、失望过，但最后都在好友们的催促下，重新爬起来，站到了操场上。 In fact, I did not build big, nor strong muscles, not a sport-born people. Over the past few years to adhere to it, because I have a team behind, the strength of a strongteam here, very grateful to our team, for a long time, we encourage each other, and with sweat, enjoying common health happy. For example, Friends of the several run in order to maintain order and unable to attend the 10，000 meters race, and they are always concerned about the brothers and promptly inform the place and time, gives us confidence and courage. At the same time, also came on their own inner desire and pursuit for a good health, who wrote many of their own log in order to refuel for their own, and inspiring. 其实我没有高大身材，也没健壮肌肉，天生不属于运动型的人。几年来能够坚持下来，因为我的背后有一个团队，有着强大团队的力量，在这里，非常感谢我们的晨跑队，长期以来，我们相互鼓励着，一起流汗，共同享受着健康带来的快
1.How to build a business that lasts100years 0:11Imagine that you are a product designer.And you've designed a product,a new type of product,called the human immune system.You're pitching this product to a skeptical,strictly no-nonsense manager.Let's call him Bob.I think we all know at least one Bob,right?How would that go? 0:34Bob,I've got this incredible idea for a completely new type of personal health product.It's called the human immune system.I can see from your face that you're having some problems with this.Don't worry.I know it's very complicated.I don't want to take you through the gory details,I just want to tell you about some of the amazing features of this product.First of all,it cleverly uses redundancy by having millions of copies of each component--leukocytes,white blood cells--before they're actually needed,to create a massive buffer against the unexpected.And it cleverly leverages diversity by having not just leukocytes but B cells,T cells,natural killer cells,antibodies.The components don't really matter.The point is that together,this diversity of different approaches can cope with more or less anything that evolution has been able to throw up.And the design is completely modular.You have the surface barrier of the human skin,you have the very rapidly reacting innate immune system and then you have the highly targeted adaptive immune system.The point is,that if one system fails,another can take over,creating a virtually foolproof system. 1:54I can see I'm losing you,Bob,but stay with me,because here is the really killer feature.The product is completely adaptive.It's able to actually develop targeted antibodies to threats that it's never even met before.It actually also does this with incredible prudence,detecting and reacting to every tiny threat,and furthermore, remembering every previous threat,in case they are ever encountered again.What I'm pitching you today is actually not a stand-alone product.The product is embedded in the larger system of the human body,and it works in complete harmony with that system,to create this unprecedented level of biological protection.So Bob,just tell me honestly,what do you think of my product? 2:47And Bob may say something like,I sincerely appreciate the effort and passion that have gone into your presentation,blah blah blah-- 2:56(Laughter) 2:58But honestly,it's total nonsense.You seem to be saying that the key selling points of your product are that it is inefficient and complex.Didn't they teach you 80-20?And furthermore,you're saying that this product is siloed.It overreacts, makes things up as it goes along and is actually designed for somebody else's benefit. I'm sorry to break it to you,but I don't think this one is a winner.
关于工作的优秀英语演讲稿 Different people have various ambitions. Some want to be engineers or doctors in the future. Some want to be scientists or businessmen. Still some wish to be teachers or lawers when they grow up in the days to come. Unlike other people, I prefer to be a farmer. However, it is not easy to be a farmer for Iwill be looked upon by others. Anyway,what I am trying to do is to make great contributions to agriculture. It is well known that farming is the basic of the country. Above all, farming is not only a challenge but also a good opportunity for the young. We can also make a big profit by growing vegetables and food in a scientific way. Besides we can apply what we have learned in school to farming. Thus our countryside will become more and more properous. I believe that any man with knowledge can do whatever they can so long as this job can meet his or her interest. All the working position can provide him with a good chance to become a talent. 1 ————来源网络整理，仅供供参考
个人先进事迹简介 01 在思想政治方面,xxxx同学积极向上,热爱祖国、热爱中国共产党,拥护中国共产党的领导.利用课余时间和党课机会认真学习政治理论,积极向党组织靠拢. 在学习上,xxxx同学认为只有把学习成绩确实提高才能为将来的实践打下扎实的基础,成为社会有用人才.学习努力、成绩优良. 在生活中,善于与人沟通,乐观向上,乐于助人.有健全的人格意识和良好的心理素质和从容、坦诚、乐观、快乐的生活态度,乐于帮助身边的同学,受到师生的好评. 02 xxx同学认真学习政治理论,积极上进,在校期间获得原院级三好生,和校级三好生,优秀团员称号,并获得三等奖学金. 在学习上遇到不理解的地方也常常向老师请教,还勇于向老师提出质疑.在完成自己学业的同时,能主动帮助其他同学解决学习上的难题,和其他同学共同探讨,共同进步. 在社会实践方面,xxxx同学参与了中国儿童文学精品“悦”读书系,插画绘制工作,xxxx同学在班中担任宣传委员,工作积极主动,认真负责,有较强的组织能力.能够在老师、班主任的指导下独立完成学院、班级布置的各项工作. 03 xxx同学在政治思想方面积极进取,严格要求自己.在学习方面刻苦努力,不断钻研,学习成绩优异,连续两年荣获国家励志奖学金；作
为一名学生干部,她总是充满激情的迎接并完成各项工作,荣获优秀团干部称号.在社会实践和志愿者活动中起到模范带头作用. 04 xxxx同学在思想方面,积极要求进步,为人诚实,尊敬师长.严格 要求自己.在大一期间就积极参加了党课初、高级班的学习,拥护中国共产党的领导,并积极向党组织靠拢. 在工作上,作为班中的学习委员,对待工作兢兢业业、尽职尽责 的完成班集体的各项工作任务.并在班级和系里能够起骨干带头作用.热心为同学服务,工作责任心强. 在学习上,学习目的明确、态度端正、刻苦努力,连续两学年在 班级的综合测评排名中获得第1.并荣获院级二等奖学金、三好生、优秀班干部、优秀团员等奖项. 在社会实践方面,积极参加学校和班级组织的各项政治活动,并 在志愿者活动中起到模范带头作用.积极锻炼身体.能够处理好学习与工作的关系,乐于助人,团结班中每一位同学,谦虚好学,受到师生的好评. 05 在思想方面,xxxx同学积极向上,热爱祖国、热爱中国共产党,拥护中国共产党的领导.作为一名共产党员时刻起到积极的带头作用,利用课余时间和党课机会认真学习政治理论. 在工作上,作为班中的团支部书记,xxxx同学积极策划组织各类 团活动,具有良好的组织能力. 在学习上,xxxx同学学习努力、成绩优良、并热心帮助在学习上有困难的同学,连续两年获得二等奖学金. 在生活中,善于与人沟通,乐观向上,乐于助人.有健全的人格意 识和良好的心理素质.
尊敬的领导，老师，亲爱的同学们， 大家好！我是5班的梁浩东。今天早上我坐车来学校的路上，我仔细观察了路上形形色色的人，有开着小车衣着精致的叔叔阿姨，有市场带着倦容的卖各种早点的阿姨，还有偶尔穿梭于人群中衣衫褴褛的乞丐。于是我问自己，十几年后我会成为怎样的自己，想成为社会成功人士还是碌碌无为的人呢，答案肯定是前者。那么十几年后我怎样才能如愿以偿呢，成为一个受人尊重，有价值的人呢？正如我今天演讲的题目是：自主管理。 大家都知道爱玩是我们孩子的天性，学习也是我们的责任和义务。要怎样处理好这些矛盾，提高自主管理呢？ 首先，我们要有小主人翁思想，自己做自己的主人，要认识到我们学习，生活这一切都是我们自己走自己的人生路，并不是为了报答父母，更不是为了敷衍老师。 我认为自主管理又可以理解为自我管理，在学习和生活中无处不在，比如通过老师，小组长来管理约束行为和同学们对自身行为的管理都属于自我管理。比如我们到一个旅游景点，看到一块大石头，有的同学特别兴奋，会想在上面刻上：某某某到此一游话。这时你就需要自我管理，你需要提醒自己，这样做会破坏景点，而且是一种素质低下的表现。你设想一下，如果别人家小孩去你家墙上乱涂乱画，你是何种感受。同样我们把自主管理放到学习上，在我们想偷懒，想逃避，想放弃的时候，我们可以通过自主管理来避免这些，通过他人或者自己的力量来完成。例如我会制定作息时间计划表，里面包括学习，运动，玩耍等内容的完成时间。那些学校学习尖子，他们学习好是智商高于我们吗，其实不然，在我所了解的哪些优秀的学霸传授经验里，就提到要能够自我管理，规范好学习时间的分分秒秒，只有辛勤的付出，才能取得优异成绩。 在现实生活中，无数成功人士告诉我们自主管理的重要性。十几年后我想成为一位优秀的，为国家多做贡献的人。亲爱的同学们，你们们？让我们从现在开始重视和执行自主管理，十几年后成为那个你想成为的人。 谢谢大家！
关于工作的英语演讲稿 【篇一：关于工作的英语演讲稿】 关于工作的英语演讲稿 different people have various ambitions. some want to be engineers or doctors in the future. some want to be scientists or businessmen. still some wish to be teachers or lawers when they grow up in the days to come. unlike other people, i prefer to be a farmer. however, it is not easy to be a farmer for iwill be looked upon by others. anyway,what i am trying to do is to make great contributions to agriculture. it is well known that farming is the basic of the country. above all, farming is not only a challenge but also a good opportunity for the young. we can also make a big profit by growing vegetables and food in a scientific way. besides we can apply what we have learned in school to farming. thus our countryside will become more and more properous. i believe that any man with knowledge can do whatever they can so long as this job can meet his or her interest. all the working position can provide him with a good chance to become a talent. 【篇二：关于责任感的英语演讲稿】 im grateful that ive been given this opportunity to stand here as a spokesman. facing all of you on the stage, i have the exciting feeling of participating in this speech competition. the topic today is what we cannot afford to lose. if you ask me this question, i must tell you that i think the answer is a word---- responsibility. in my elementary years, there was a little girl in the class who worked very hard, however she could never do satisfactorily in her lessons. the teacher asked me to help her, and it was obvious that she expected a lot from me. but as a young boy, i was so restless and thoughtless, i always tried to get more time to play and enjoy myself. so she was always slighted over by me. one day before the final exam, she came up to me and said, could you please explain this to me? i can not understand it. i
Dear teacher and colleagues: my topic is on “spare time”. It is a huge blessing that we can work 996. Jack Ma said at an Ali's internal communication activity, That means we should work at 9am to 9pm, 6 days a week .I question the entire premise of this piece. but I'm always interested in hearing what successful and especially rich people come up with time .So I finally found out Jack Ma also had said :”i f you don’t put out more time and energy than others ,how can you achieve the success you want? If you do not do 996 when you are young ,when will you ?”I quite agree with the idea that young people should fight for success .But there are a lot of survival activities to do in a day ,I want to focus on how much time they take from us and what can we do with the rest of the time. As all we known ,There are 168 hours in a week .We sleep roughly seven-and-a-half and eight hours a day .so around 56 hours a week . maybe it is slightly different for someone . We do our personal things like eating and bathing and maybe looking after kids -about three hours a day .so around 21 hours a week .And if you are working a full time job ,so 40 hours a week , Oh! Maybe it is impossible for us at
关于人英语演讲稿(精选多篇) 关于人的优美句子 1、“黑皮小子”是我对在公交车上偶遇两次的一个男孩的称呼代号。一听这个外号，你也定会知道他极黑了。他的脸总是黑黑的；裸露在短袖外的胳膊也是黑黑的；就连两只有厚厚耳垂的耳朵也那么黑黑的，时不时像黑色的猎犬竖起来倾听着什么；黑黑的扁鼻子时不时地深呼吸着，像是在警觉地嗅着什么异样的味道。 2、我不知道，如何诠释我的母亲，因为母亲淡淡的生活中却常常跳动着不一样的间最无私、最伟大、最崇高的爱，莫过于母爱。无私，因为她的爱只有付出，无需回报；伟大，因为她的爱寓于
普通、平凡和简单之中；崇高，是因为她的爱是用生命化作乳汁，哺育着我，使我的生命得以延续，得以蓬勃，得以灿烂。 3、我的左撇子伙伴是用左手写字的，就像我们的右手一样挥洒自如。在日常生活中，曾见过用左手拿筷子的，也有像超级林丹用左手打羽毛球的，但很少碰见用左手写字的。中国汉字笔画笔顺是左起右收，适合用右手写字。但我的左撇子伙伴写字是右起左收的，像鸡爪一样迈出田字格，左看右看，上看下看，每一个字都很难看。平时考试时间终了，他总是做不完试卷。于是老师就跟家长商量，决定让他左改右写。经过老师引导，家长配合，他自己刻苦练字，考试能够提前完成了。现在他的字像他本人一样阳光、帅气。 4、老师，他们是辛勤的园丁，帮助着那些幼苗茁壮成长。他们不怕辛苦地给我们改厚厚一叠的作业，给我们上课。一步一步一点一点地给我们知识。虽然
他们有时也会批评一些人，但是他们的批评是对我们有帮助的，我们也要理解他们。那些学习差的同学，老师会逐一地耐心教导，使他们的学习突飞猛进。使他们的耐心教导培养出了一批批优秀的人才。他们不怕辛苦、不怕劳累地教育着我们这些幼苗，难道不是美吗？ 5、我有一个表妹，还不到十岁，她那圆圆的小脸蛋儿，粉白中透着粉红，她的头发很浓密，而且好像马鬓毛一样的粗硬，但还是保留着孩子一样的蓬乱的美，卷曲的环绕着她那小小的耳朵。说起她，她可是一个古灵精怪的小女孩。 6、黑皮小子是一个善良的人，他要跟所有见过的人成为最好的朋友！这样人人都是他的好朋友，那么人人都是好友一样坦诚、关爱相交，这样人与人自然会和谐起来，少了许多争执了。 7、有人说，老师是土壤，把知识化作养分，传授给祖国的花朵，让他们茁壮成长。亦有人说，老师是一座知识的桥梁，把我们带进奇妙的科学世界，让
优秀党务工作者事迹简介范文 优秀党务工作者事迹简介范文 ***，男，198*年**月出生，200*年加入党组织，现为***支部书记。从事党务工作以来，兢兢业业、恪尽职守、辛勤工作，出色地完成了各项任务，在思想上、政治上同党中央保持高度一致，在业务上不断进取，团结同事，在工作岗位上取得了一定成绩。 一、严于律己，勤于学习 作为一名党务工作者，平时十分注重知识的更新，不断加强党的理论知识的学习，坚持把学习摆在重要位置，学习领会和及时掌握党和国家的路线、方针、政策，特别是党的十九大精神，注重政治理论水平的提高，具有坚定的理论信念;坚持党的基本路线，坚决执行党的各项方针政策，自觉履行党员义务，正确行使党员权利。平时注重加强业务和管理知识的学习，并运用到工作中去，不断提升自身工作能力，具有开拓创新精神，在思想上、政治上和行动上时刻同党中央保持高度一致。 二、求真务实，开拓进取 在工作中任劳任怨，踏实肯干，坚持原则，认真做好学院的党务工作，按照党章的要求，严格发展党员的每一个步骤，认真细致的对待每一份材料。配合党总支书记做好学院的党建工作，完善党总支建设方面的文件、材料和工作制度、管理制度等。
三、生活朴素，乐于助人 平时重视与同事间的关系，主动与同事打成一片，善于发现他人的难处，及时妥善地给予帮助。在其它同志遇到困难时，积极主动伸出援助之手，尽自己最大努力帮助有需要的人。养成了批评与自我批评的优良作风，时常反省自己的工作，学习和生活。不但能够真诚的指出同事的缺点，也能够正确的对待他人的批评和意见。面对误解，总是一笑而过，不会因为误解和批评而耿耿于怀，而是诚恳的接受，从而不断的提高自己。在生活上勤俭节朴，不铺张浪费。 身为一名老党员，我感到责任重大，应该做出表率，挤出更多的时间来投入到**党总支的工作中，不找借口，不讲条件，不畏困难，将总支建设摆在更重要的位置，解开工作中的思想疙瘩，为攻坚克难铺平道路，以支部为纽带，像战友一样团结，像家庭一样维系，像亲人一样关怀，践行入党誓言。把握机遇，迎接挑战，不负初心。
关于时间的英语演讲稿范文_演讲稿 and organizing people to learn from advanced areas to broaden their horizons in order to understand the team of cadres working conditions in schools, the ministry of education has traveled a number of primary and secondary schools to conduct research, listen to the views of the school party and government leaders to make school leadership cadres receive attention and guidance of the ministry of education to carry out a variety of practical activities to actively lead the majority of young teachers work hard to become qualified personnel and for them to put up the cast talent stage, a single sail swaying, after numerous twists and turns arrived in port, if there is wind, a hand, and naturally smooth arrival and guide students to strive to e xcel, need to nazhen “wind” - teacher. teachers should be ideological and moral education, culture education and the needs of students organically combining various activities for the students or students to carry out their own. for example: school quiz competitions, essay contests, ke benju performances and other activities to enable students to give full play to their talents. teachers rush toil, in order to that will enable students to continue to draw nutrients, to help them grow up healthily and become pillars of the country before. for all students in general education, the government departments have also not forget those who cared about the