Abstract-This paper describes extensions to the PAPI hardware counter library for virtual environments, called PAPI-V. The extensions support timing routines, I/O measurements, and processor counters. The PAPI-V extensions will allow application and tool developers to use a familiar interface to obtain relevant hardware performance monitoring information in virtual environments. Keywords-performance counters; virutal machines; performance analsis; performance monitoring I.INTRODUCTION Cloud computing involves use of a hosted computational environment that can provide elastic compute and storage services on demand. Virtualization is a technology that allows multiple virtual machines (VMs) to run on a single physical machine and share its resources. Virtualization is increasingly being used in cloud computing to provide economies of scale, customized environments, fault isolation, and reliability. To address performance concerns with the use of cloud computing for scientific computing, the PAPI-V project is developing a system for hardware performance monitoring in virtualized environments to enable software developers to understand and optimize system and application performance and adapt to changing conditions. To accomplish this goal, the project is extending the widely used Performance API (PAPI) crossplatform library [PAPI] for accessing hardware performance counters [1].
The Blue Gene/Q (BG/Q) system is the third generation in the IBM Blue Gene line of massively parallel, energy efficient supercomputers that increases not only in size but also in complexity compared to its Blue Gene predecessors. Consequently, gaining insight into the intricate ways in which software and hardware are interacting requires richer and more capable performance analysis methods in order to be able to improve efficiency and scalability of applications that utilize this advanced system. The BG/Q predecessor, Blue Gene/P, suffered from incompletely implemented hardware performance monitoring tools. To address these limitations, an industry/academic collaboration was established early in BG/Q's development cycle to insure the delivery of effective performance tools at the machine's introduction. An extensive effort has been made to extend the Performance API (PAPI) to support hardware performance monitoring for the BG/Q platform. This paper provides detailed information about five recently added PAPI components that allow hardware performance counter monitoring of the 5D-Torus network, the I/O system and the Compute Node Kernel in addition to the processing cores on BG/Q. Furthermore, we explore the impact of node mappings on the performance of a parallel 3D-FFT kernel and use the new PAPI network component to collect hardware performance counter data on the 5D-Torus network. As a result, the network counters detected a large amount of redundant inter-node communications, which we were able to completely eliminate with the use of a customized node mapping.
Abstract-For more than a decade, the PAPI performancemonitoring library has provided a clear, portable interface to the hardware performance counters available on all modern CPUs and other components of interest (e.g., GPUs, network, and I/O systems). Most major end-user tools that application developers use to analyze the performance of their applications rely on PAPI to gain access to these performance counters.One of the critical roadblocks on the way to larger, more complex high performance systems, has been widely identified as being the energy efficiency constraints. With modern extreme scale machines having hundreds of thousands of cores, the ability to reduce power consumption for each CPU at the software level becomes critically important, both for economic and environmental reasons. In order for PAPI to continue playing its well established role in HPC, it is pressing to provide valuable performance data that not only originates from within the processing cores but also delivers insight into the power consumption of the system as a whole.An extensive effort has been made to extend the Performance API to support power monitoring capabilities for various platforms. This paper provides detailed information about three components that allow power monitoring on the Intel Xeon Phi and Blue Gene/Q. Furthermore, we discuss the integration of PAPI in PARSEC -a task-based dataflow-driven execution engine -enabling hardware performance counter and power monitoring at true task granularity.
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