The Experts below are selected from a list of 45966 Experts worldwide ranked by ideXlab platform
Ce-kuen Shieh - One of the best experts on this subject based on the ideXlab platform.
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Incorporating Memory Resource considerations into the workload distribution of software DSM systems
Journal of Information Science and Engineering, 2007Co-Authors: Yen-tso Liu, Tyng-yeu Liang, Jyh-biau Chang, Ce-kuen ShiehAbstract:Conventional workload distribution schemes for software distributed shared Memory (DSM) systems simply distribute the program threads in accordance with the CPU power of the individual processors or the data-sharing characteristics of the application. Although these schemes aim to minimize the program execution time by reducing the computation and communication costs, Memory access costs also have a major influence on the overall program performance. If a processor has insufficient physical Memory space to cache all of the data required by its local working threads, it must perform a series of page replacements if it is to complete its thread executions. Although these page replacements enable the threads to complete their tasks, thread execution is inevitably delayed by the latency of the page swapping operations. Consequently, the current study proposes a novel workload distribution scheme for DSM systems which considers not only the CPU power and data-sharing characteristics, but also the physical Memory capabilities of the individual processors. The present results confirm the importance of considering Memory Resources when establishing an appropriate workload distribution for DSM systems and indicate that the proposed scheme is more effective than schemes which consider only CPU Resources or Memory Resources, respectively.
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CLUSTER - Adding Memory Resource consideration into workload distribution for software DSM systems
Proceedings IEEE International Conference on Cluster Computing CLUSTR-03, 2003Co-Authors: Tyng-yeu Liang, Yen-tao Liu, Ce-kuen ShiehAbstract:Workload distribution is an important issue for user applications to obtain a good performance on software distributed shared Memory (DSM) systems. The workload distribution methods that were previously proposed for DSM systems always considered only CPU power and data sharing but ignored Memory availability of processors. However, the cost of Memory accesses is also an important factor of determining program performance especially when processors have not sufficient Memory space to cache all the data needed by program threads. Therefore, the past methods might make wrong decisions in workload distribution to degrade program performance. To resolve this problem, this paper is dedicated to adding Memory Resource consideration into workload distribution for software DSM systems. Our experimental result shows that one workload distribution method with Memory Resource consideration is indeed more effective for minimizing the execution time of DSM applications than another without Memory Resource consideration.
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CCGRID - Involving Memory Resource consideration into workload distribution for software DSM systems
IEEE International Symposium on Cluster Computing and the Grid 2004. CCGrid 2004., 1Co-Authors: Yen-tso Liu, Tyng-yeu Liang, Zhe-hung Kuo, Ce-kuen ShiehAbstract:A suitable workload distribution is essential for obtaining good performance on software distributed shared Memory (DSM) systems. The methods proposed by the past DSM researches only simply distribute program threads in accordance with the CPU power of the individual processors or the data sharing within the DSM applications. These methods expected the minimal program execution time by reducing computation cost and communication cost. However, Memory access costs also play a significant role in determining the program performance. If a processor lacks sufficient Memory space to cache all of the data required by the working threads, then it will suffer from a series of page faults and will need to execute consecutive page replacements while executing its threads. Although the threads will be able to complete their tasks, their execution will inevitably be delayed by the latency associated with executing the page replacements. To resolve this problem, this paper is dedicated to involving Memory Resource consideration into workload distribution for software DSM systems. The present results confirm the importance of considering Memory Resources when addressing the workload distribution of DSM systems. It is shown that the proposed method is more effective than previous methods which considered only CPU Resource or Memory Resource.
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ICPP Workshops - Memory Resource considerations in the load balancing of software DSM systems
2003 International Conference on Parallel Processing Workshops 2003. Proceedings., 1Co-Authors: Yen-tso Liu, Tyng-yeu Liang, Chi-ting Huang, Ce-kuen ShiehAbstract:Obtaining a load balance is essential if the program performance of distributed shared Memory (DSM) systems is to be optimized. In achieving this load balance, most DSM systems simply distribute program threads in accordance with the CPU power of the individual processors within the network. However, Memory access costs also play a significant role in determining the program performance. Although the threads will be able to complete their tasks, their execution will inevitably be delayed by the latency associated with executing the page replacements. With the rapid development of CPU chips, the relative influence of this Memory access latency upon the overall program performance has become increasingly significant. Therefore, attempts to minimize the execution time of applications by establishing a load balance based purely upon CPU Resource considerations will only achieve limited success. The current study proposes a new load balancing scheme for DSM systems which considers both CPU and Memory Resources. The present results confirm the importance of considering Memory Resources when addressing the load balancing of DSM systems. It is shown that the proposed method is more effective than previous schemes which considered only CPU Resources or Memory Resources.
Ouhao Chen - One of the best experts on this subject based on the ideXlab platform.
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extending cognitive load theory to incorporate working Memory Resource depletion evidence from the spacing effect
Educational Psychology Review, 2018Co-Authors: Juan C Castroalonso, Ouhao Chen, Fred Paas, John SwellerAbstract:Depletion of limited working Memory Resources may occur following extensive mental effort resulting in decreased performance compared to conditions requiring less extensive mental effort. This “depletion effect” can be incorporated into cognitive load theory that is concerned with using the properties of human cognitive architecture, especially working Memory, when designing instruction. Two experiments were carried out on the spacing effect that occurs when learning that is spaced by temporal gaps between learning episodes is superior to identical, massed learning with no gaps between learning episodes. Using primary school students learning mathematics, it was found that students obtained lower scores on a working Memory capacity test (Experiments 1 and 2) and higher ratings of cognitive load (Experiment 2) after massed than after spaced practice. The reduction in working Memory capacity may be attributed to working Memory Resource depletion following the relatively prolonged mental effort associated with massed compared to spaced practice. An expansion of cognitive load theory to incorporate working Memory Resource depletion along with instructional design implications, including the spacing effect, is discussed.
Tyng-yeu Liang - One of the best experts on this subject based on the ideXlab platform.
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Incorporating Memory Resource considerations into the workload distribution of software DSM systems
Journal of Information Science and Engineering, 2007Co-Authors: Yen-tso Liu, Tyng-yeu Liang, Jyh-biau Chang, Ce-kuen ShiehAbstract:Conventional workload distribution schemes for software distributed shared Memory (DSM) systems simply distribute the program threads in accordance with the CPU power of the individual processors or the data-sharing characteristics of the application. Although these schemes aim to minimize the program execution time by reducing the computation and communication costs, Memory access costs also have a major influence on the overall program performance. If a processor has insufficient physical Memory space to cache all of the data required by its local working threads, it must perform a series of page replacements if it is to complete its thread executions. Although these page replacements enable the threads to complete their tasks, thread execution is inevitably delayed by the latency of the page swapping operations. Consequently, the current study proposes a novel workload distribution scheme for DSM systems which considers not only the CPU power and data-sharing characteristics, but also the physical Memory capabilities of the individual processors. The present results confirm the importance of considering Memory Resources when establishing an appropriate workload distribution for DSM systems and indicate that the proposed scheme is more effective than schemes which consider only CPU Resources or Memory Resources, respectively.
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CLUSTER - Adding Memory Resource consideration into workload distribution for software DSM systems
Proceedings IEEE International Conference on Cluster Computing CLUSTR-03, 2003Co-Authors: Tyng-yeu Liang, Yen-tao Liu, Ce-kuen ShiehAbstract:Workload distribution is an important issue for user applications to obtain a good performance on software distributed shared Memory (DSM) systems. The workload distribution methods that were previously proposed for DSM systems always considered only CPU power and data sharing but ignored Memory availability of processors. However, the cost of Memory accesses is also an important factor of determining program performance especially when processors have not sufficient Memory space to cache all the data needed by program threads. Therefore, the past methods might make wrong decisions in workload distribution to degrade program performance. To resolve this problem, this paper is dedicated to adding Memory Resource consideration into workload distribution for software DSM systems. Our experimental result shows that one workload distribution method with Memory Resource consideration is indeed more effective for minimizing the execution time of DSM applications than another without Memory Resource consideration.
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CCGRID - Involving Memory Resource consideration into workload distribution for software DSM systems
IEEE International Symposium on Cluster Computing and the Grid 2004. CCGrid 2004., 1Co-Authors: Yen-tso Liu, Tyng-yeu Liang, Zhe-hung Kuo, Ce-kuen ShiehAbstract:A suitable workload distribution is essential for obtaining good performance on software distributed shared Memory (DSM) systems. The methods proposed by the past DSM researches only simply distribute program threads in accordance with the CPU power of the individual processors or the data sharing within the DSM applications. These methods expected the minimal program execution time by reducing computation cost and communication cost. However, Memory access costs also play a significant role in determining the program performance. If a processor lacks sufficient Memory space to cache all of the data required by the working threads, then it will suffer from a series of page faults and will need to execute consecutive page replacements while executing its threads. Although the threads will be able to complete their tasks, their execution will inevitably be delayed by the latency associated with executing the page replacements. To resolve this problem, this paper is dedicated to involving Memory Resource consideration into workload distribution for software DSM systems. The present results confirm the importance of considering Memory Resources when addressing the workload distribution of DSM systems. It is shown that the proposed method is more effective than previous methods which considered only CPU Resource or Memory Resource.
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ICPP Workshops - Memory Resource considerations in the load balancing of software DSM systems
2003 International Conference on Parallel Processing Workshops 2003. Proceedings., 1Co-Authors: Yen-tso Liu, Tyng-yeu Liang, Chi-ting Huang, Ce-kuen ShiehAbstract:Obtaining a load balance is essential if the program performance of distributed shared Memory (DSM) systems is to be optimized. In achieving this load balance, most DSM systems simply distribute program threads in accordance with the CPU power of the individual processors within the network. However, Memory access costs also play a significant role in determining the program performance. Although the threads will be able to complete their tasks, their execution will inevitably be delayed by the latency associated with executing the page replacements. With the rapid development of CPU chips, the relative influence of this Memory access latency upon the overall program performance has become increasingly significant. Therefore, attempts to minimize the execution time of applications by establishing a load balance based purely upon CPU Resource considerations will only achieve limited success. The current study proposes a new load balancing scheme for DSM systems which considers both CPU and Memory Resources. The present results confirm the importance of considering Memory Resources when addressing the load balancing of DSM systems. It is shown that the proposed method is more effective than previous schemes which considered only CPU Resources or Memory Resources.
Clayton E. Curtis - One of the best experts on this subject based on the ideXlab platform.
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Strategic allocation of working Memory Resource.
Scientific reports, 2018Co-Authors: Aspen H. Yoo, Zuzanna Klyszejko, Clayton E. CurtisAbstract:Visual working Memory (VWM), the brief retention of past visual information, supports a range of cognitive functions. One of the defining, and largely studied, characteristics of VWM is how Resource-limited it is, raising questions about how this Resource is shared or split across memoranda. Since objects are rarely equally important in the real world, we ask how people split this Resource in settings where objects have different levels of importance. In a psychophysical experiment, participants remembered the location of four targets with different probabilities of being tested after a delay. We then measured their Memory accuracy of one of the targets. We found that participants allocated more Resource to memoranda with higher priority, but underallocated Resource to high- and overallocated to low-priority targets relative to the true probability of being tested. These results are well explained by a computational model in which Resource is allocated to minimize expected estimation error. We replicated this finding in a second experiment in which participants bet on their Memory fidelity after making the location estimate. The results of this experiment show that people have access to and utilize the quality of their Memory when making decisions. Furthermore, people again allocate Resource in a way that minimizes Memory errors, even in a context in which an alternative strategy was incentivized. Our study not only shows that people are allocating Resource according to behavioral relevance, but suggests that they are doing so with the aim of maximizing Memory accuracy.
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Strategic allocation of working Memory Resource
2018Co-Authors: Aspen H. Yoo, Zuzanna Klyszejko, Clayton E. CurtisAbstract:Visual working Memory (VWM), the brief retention of past visual information, supports a range of cognitive functions (Fukuda, Vogel, Mayr, & Awh, 2010; Johnson et al., 2013). The Resource that supports VWM is limited, raising the question of how the brain allocates this limited Resource to different objects. This question is even more interesting in ecological settings, in which objects are not equally important. In a psychophysical experiment, participants remembered the location of four targets with different probabilities of being tested after a delay. We then measured their Memory accuracy of one of the targets. We found that participants allocated more Resource to memoranda with higher priority, but underallocated Resource to high- and overallocated to low-priority targets relative to the true probe probabilities. These results are well explained by a computational model in which Resource is allocated to minimize expected estimation error. We replicated this finding in a second experiment in which participants bet on their Memory fidelity after making the location estimate. The results of this experiment show that people 1) use information about Memory quality and 2) minimize error even with an incentivized, alternative Resource allocation strategy. Humans may mitigate the behavioral effects of a limited VWM through knowledge of Memory fidelity and strategic Resource allocation.
John Sweller - One of the best experts on this subject based on the ideXlab platform.
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extending cognitive load theory to incorporate working Memory Resource depletion evidence from the spacing effect
Educational Psychology Review, 2018Co-Authors: Juan C Castroalonso, Ouhao Chen, Fred Paas, John SwellerAbstract:Depletion of limited working Memory Resources may occur following extensive mental effort resulting in decreased performance compared to conditions requiring less extensive mental effort. This “depletion effect” can be incorporated into cognitive load theory that is concerned with using the properties of human cognitive architecture, especially working Memory, when designing instruction. Two experiments were carried out on the spacing effect that occurs when learning that is spaced by temporal gaps between learning episodes is superior to identical, massed learning with no gaps between learning episodes. Using primary school students learning mathematics, it was found that students obtained lower scores on a working Memory capacity test (Experiments 1 and 2) and higher ratings of cognitive load (Experiment 2) after massed than after spaced practice. The reduction in working Memory capacity may be attributed to working Memory Resource depletion following the relatively prolonged mental effort associated with massed compared to spaced practice. An expansion of cognitive load theory to incorporate working Memory Resource depletion along with instructional design implications, including the spacing effect, is discussed.