The Experts below are selected from a list of 33 Experts worldwide ranked by ideXlab platform
Amir Roth - One of the best experts on this subject based on the ideXlab platform.
-
energy effectiveness of pre Execution and energy aware p Thread selection
International Symposium on Computer Architecture, 2005Co-Authors: Vlad Petric, Amir RothAbstract:Pre-Execution removes the microarchitectural latency of "problem" loads from a programýs critical path by redundantly executing copies of their computations in parallel with the main program. There have been several proposed pre-Execution systems, a quantitative framework (PTHSEL) for analytical pre-Execution Thread (p-Thread) selection, and even a research prototype. To date, however, the energy aspects of pre-Execution have not been studied. Cycle-level performance and energy simulations on SPEC2000 integer benchmarks that suffer from L2 misses show that energy-blind pre-Execution naturally has a linear latency/energy trade-off, improving performance by 13.8% while increasing energy consumption by 11.9%. To improve this trade-off, we propose two extensions to PTHSEL. First, we replace the flat cycle-for-cycle load cost model with a model based on a critical-path estimation. This extension increases p-Thread efficiency in an energy-independent way. Second, we add a parameterized energy model to PTHSEL (forming PTHSEL+E) that allows it to actively select p-Threads that reduce energy rather than (or in combination with) Execution latency. Experiments show that PTHSEL+E manipulates pre-Executionýs latency/energy more effectively. Latency targeted selection benefits from the improved load cost model: its performance improvements grow to an average of 16.4% while energy costs drop to 8.7%. ED targeted selection produces p-Threads that improve performance by only 12.9%, but ED by 8.8%. Targeting p-Thread selection for energy reduction, results in "energy-free" pre-Execution, with average speedup of 5.4%, and a small decrease in total energy consumption (0.7%).
-
a quantitative framework for automated pre Execution Thread selection
International Symposium on Microarchitecture, 2002Co-Authors: Amir Roth, Gurindar S SohiAbstract:Pre-Execution attacks cache misses for which address prediction driven prefetching fails. In pre-Execution, copies of cache miss computations are isolated from the main program and launched as separate Threads called p-Threads whenever the processor anticipates an upcoming miss. P-Thread selection is the task of deciding what computations should execute as p-Threads and when they should be launched such that total Execution time is minimized. It is central to the success of pre-Execution. We introduce a framework for automated static p-Thread selection, a static p-Thread being one whose dynamic instances are repeatedly launched during the course of program Execution. Our approach is to formalize the problem quantitatively and then apply standard techniques to solve it analytically. The framework has two novel components. The slice tree is a data structure that compactly represents a set of static p-Threads and the relationships among them. Aggregate advantage is a formula that uses raw program statistics and computation structure to assign each candidate static p-Thread a numeric score based on estimated latency tolerance and overhead aggregated over its expected dynamic Executions. We use the framework to select p-Threads that cover L2 misses and study its effectiveness under different conditions via detailed simulation. We measure the effect of constraining p-Thread length, locally optimizing p-Threads, using different program samples as a statistical basis for selection, and varying several machine parameters. Our framework responds to these changes in an intuitive way. We also validate that aggregate advantage correctly models actual pre-Execution.
Gurindar S Sohi - One of the best experts on this subject based on the ideXlab platform.
-
a quantitative framework for automated pre Execution Thread selection
International Symposium on Microarchitecture, 2002Co-Authors: Amir Roth, Gurindar S SohiAbstract:Pre-Execution attacks cache misses for which address prediction driven prefetching fails. In pre-Execution, copies of cache miss computations are isolated from the main program and launched as separate Threads called p-Threads whenever the processor anticipates an upcoming miss. P-Thread selection is the task of deciding what computations should execute as p-Threads and when they should be launched such that total Execution time is minimized. It is central to the success of pre-Execution. We introduce a framework for automated static p-Thread selection, a static p-Thread being one whose dynamic instances are repeatedly launched during the course of program Execution. Our approach is to formalize the problem quantitatively and then apply standard techniques to solve it analytically. The framework has two novel components. The slice tree is a data structure that compactly represents a set of static p-Threads and the relationships among them. Aggregate advantage is a formula that uses raw program statistics and computation structure to assign each candidate static p-Thread a numeric score based on estimated latency tolerance and overhead aggregated over its expected dynamic Executions. We use the framework to select p-Threads that cover L2 misses and study its effectiveness under different conditions via detailed simulation. We measure the effect of constraining p-Thread length, locally optimizing p-Threads, using different program samples as a statistical basis for selection, and varying several machine parameters. Our framework responds to these changes in an intuitive way. We also validate that aggregate advantage correctly models actual pre-Execution.
Vlad Petric - One of the best experts on this subject based on the ideXlab platform.
-
energy effectiveness of pre Execution and energy aware p Thread selection
International Symposium on Computer Architecture, 2005Co-Authors: Vlad Petric, Amir RothAbstract:Pre-Execution removes the microarchitectural latency of "problem" loads from a programýs critical path by redundantly executing copies of their computations in parallel with the main program. There have been several proposed pre-Execution systems, a quantitative framework (PTHSEL) for analytical pre-Execution Thread (p-Thread) selection, and even a research prototype. To date, however, the energy aspects of pre-Execution have not been studied. Cycle-level performance and energy simulations on SPEC2000 integer benchmarks that suffer from L2 misses show that energy-blind pre-Execution naturally has a linear latency/energy trade-off, improving performance by 13.8% while increasing energy consumption by 11.9%. To improve this trade-off, we propose two extensions to PTHSEL. First, we replace the flat cycle-for-cycle load cost model with a model based on a critical-path estimation. This extension increases p-Thread efficiency in an energy-independent way. Second, we add a parameterized energy model to PTHSEL (forming PTHSEL+E) that allows it to actively select p-Threads that reduce energy rather than (or in combination with) Execution latency. Experiments show that PTHSEL+E manipulates pre-Executionýs latency/energy more effectively. Latency targeted selection benefits from the improved load cost model: its performance improvements grow to an average of 16.4% while energy costs drop to 8.7%. ED targeted selection produces p-Threads that improve performance by only 12.9%, but ED by 8.8%. Targeting p-Thread selection for energy reduction, results in "energy-free" pre-Execution, with average speedup of 5.4%, and a small decrease in total energy consumption (0.7%).
Florent Teichteil-königsbuch - One of the best experts on this subject based on the ideXlab platform.
-
AMPLE: an anytime planning and Execution framework for dynamic and uncertain problems in robotics
Autonomous Robots, 2018Co-Authors: Caroline Ponzoni carvalho chanel, Jorrit T’hooft, Alexandre Albore, Charles Lesire, Florent Teichteil-königsbuchAbstract:Acting in robotics is driven by reactive and deliberative reasonings which take place in the competition between Execution and planning processes. Properly balancing reactivity and deliberation is still an open question for harmonious Execution of deliberative plans in complex robotic applications. We propose a flexible algorithmic framework to allow continuous real-time planning of complex tasks in parallel of their Executions. Our framework, named AMPLE, is oriented towards robotic modular architectures in the sense that it turns planning algorithms into services that must be generic, reactive, and valuable. Services are optimized actions that are delivered at precise time points following requests from other modules that include states and dates at which actions are needed. To this end, our framework is divided in two concurrent processes: a planning Thread which receives planning requests and delegates action selection to embedded planning softwares in compliance with the queue of internal requests, and an Execution Thread which orchestrates these planning requests as well as action Execution and state monitoring. We show how the behavior of the Execution Thread can be parametrized to achieve various strategies which can differ, for instance, depending on the distribution of internal planning requests over possible future Execution states in anticipation of the uncertain evolution of the system, or over different underlying planners to take several levels into account. We demonstrate the flexibility and the relevance of our framework on various robotic benchmarks and real experiments that involve complex planning problems of different natures which could not be properly tackled by existing dedicated planning approaches which rely on the standard plan-then-execute loop.
Caroline Ponzoni carvalho chanel - One of the best experts on this subject based on the ideXlab platform.
-
AMPLE: an anytime planning and Execution framework for dynamic and uncertain problems in robotics
Autonomous Robots, 2018Co-Authors: Caroline Ponzoni carvalho chanel, Jorrit T’hooft, Alexandre Albore, Charles Lesire, Florent Teichteil-königsbuchAbstract:Acting in robotics is driven by reactive and deliberative reasonings which take place in the competition between Execution and planning processes. Properly balancing reactivity and deliberation is still an open question for harmonious Execution of deliberative plans in complex robotic applications. We propose a flexible algorithmic framework to allow continuous real-time planning of complex tasks in parallel of their Executions. Our framework, named AMPLE, is oriented towards robotic modular architectures in the sense that it turns planning algorithms into services that must be generic, reactive, and valuable. Services are optimized actions that are delivered at precise time points following requests from other modules that include states and dates at which actions are needed. To this end, our framework is divided in two concurrent processes: a planning Thread which receives planning requests and delegates action selection to embedded planning softwares in compliance with the queue of internal requests, and an Execution Thread which orchestrates these planning requests as well as action Execution and state monitoring. We show how the behavior of the Execution Thread can be parametrized to achieve various strategies which can differ, for instance, depending on the distribution of internal planning requests over possible future Execution states in anticipation of the uncertain evolution of the system, or over different underlying planners to take several levels into account. We demonstrate the flexibility and the relevance of our framework on various robotic benchmarks and real experiments that involve complex planning problems of different natures which could not be properly tackled by existing dedicated planning approaches which rely on the standard plan-then-execute loop.