Abstract:
Particular techniques for improving the scalability of concurrent programs (e.g., lock-based applications) may be effective in some environments and for some workloads, but not others. The systems described herein may automatically choose appropriate ones of these techniques to apply when executing lock-based applications at runtime, based on observations of the application in the current environment and with the current workload. In one example, two techniques for improving lock scalability (e.g., transactional lock elision using hardware transactional memory, and optimistic software techniques) may be integrated together. A lightweight runtime library built for this purpose may adapt its approach to managing concurrency by dynamically selecting one or more of these techniques (at different times) during execution of a given application. In this Adaptive Lock Elision approach, the techniques may be selected (based on pluggable policies) at runtime to achieve good performance on different platforms and for different workloads.
Abstract:
Particular techniques for improving the scalability of concurrent programs (e.g., lock-based applications) may be effective in some environments and for some workloads, but not others. The systems described herein may automatically choose appropriate ones of these techniques to apply when executing lock-based applications at runtime, based on observations of the application in the current environment and with the current workload. In one example, two techniques for improving lock scalability (e.g., transactional lock elision using hardware transactional memory, and optimistic software techniques) may be integrated together. A lightweight runtime library built for this purpose may adapt its approach to managing concurrency by dynamically selecting one or more of these techniques (at different times) during execution of a given application. In this Adaptive Lock Elision approach, the techniques may be selected (based on pluggable policies) at runtime to achieve good performance on different platforms and for different workloads.
Abstract:
Particular techniques for improving the scalability of concurrent programs (e.g., lock-based applications) may be effective in some environments and for some workloads, but not others. The systems described herein may automatically choose appropriate ones of these techniques to apply when executing lock-based applications at runtime, based on observations of the application in the current environment and with the current workload. In one example, two techniques for improving lock scalability (e.g., transactional lock elision using hardware transactional memory, and optimistic software techniques) may be integrated together. A lightweight runtime library built for this purpose may adapt its approach to managing concurrency by dynamically selecting one or more of these techniques (at different times) during execution of a given application. In this Adaptive Lock Elision approach, the techniques may be selected (based on pluggable policies) at runtime to achieve good performance on different platforms and for different workloads.
Abstract:
Particular techniques for improving the scalability of concurrent programs (e.g., lock-based applications) may be effective in some environments and for some workloads, but not others. The systems described herein may automatically choose appropriate ones of these techniques to apply when executing lock-based applications at runtime, based on observations of the application in the current environment and with the current workload. In one example, two techniques for improving lock scalability (e.g., transactional lock elision using hardware transactional memory, and optimistic software techniques) may be integrated together. A lightweight runtime library built for this purpose may adapt its approach to managing concurrency by dynamically selecting one or more of these techniques (at different times) during execution of a given application. In this Adaptive Lock Elision approach, the techniques may be selected (based on pluggable policies) at runtime to achieve good performance on different platforms and for different workloads.