1. libperf libperf provides a tracing interface into the Linux Kernel Performance Counters (LKPC) subsystem recently introduced into the Linux Kernel mainline. This interface provides a unified API abstracting hardware- based performance counters, kernel trace points, and software-defined trace points. The counters are maintained by the kernel and maintain statistics per thread and per core. All counters are “virtual” 64-bit integers and are accessed via special file descriptors obtained from the kernel within libperf. Features and Highlights System Call Wrapper Library First API for LKPC First User Space Library Interfacing with LKPC Simple C API – 2 Calls Required by Default Efficient Kernel Implementation Low Overhead Feasible for Dynamic Feedback Preparing for Open Source GPLv2 Release Code Example … /* start of tracing */ struct perf_data* pd = libperf_initialize(-1,-1); … /* do work */ libperf_finalize(pd, UUID); … /* end of tracing */ Performance Overhead Evaluated Using sysbench 10 Runs Averaged on an Intel Centrino 2 Overhead Significant for Threading (Context Switching) Worst Case: 3.63 % Average Case:3.25 % Best Case:2.87 % LightSpeed: Task Scheduling for Many-Core Karl Naden (kbn@cs.cmu.edu)Wolfgang Richter (wolf@cs.cmu.edu)Ekaterina Taralova (etaralova@cs.cmu.edu) Introduction Parallel applications have a hard time taking advantage of specifics of hardware. Operating Systems have greater knowledge of the hardware, but lose application-specific data. Solutions cutting across the stack from software to hardware may offer compelling paths in the future. Approach Provide the application layer more control over scheduling tasks and provide detailed information about hardware performance to make informed decisions based on application knowledge. Questions: 1.How could statistics about the underlying architecture’s performance be delivered efficiently to applications? 2.How could applications take advantage of this additional information? Target Workload Overview Machine Learning Parallel Algorithm Framework Tailored to Iterative Algorithms on Graph Data Structures GraphLab Key Components and Inputs Why GraphLab? Existing Parallel Scheduling Problem Specific Problem Formulation—Graphs Significant Variation in Algorithms Gives Potential for Generality References [1] Yucheng Low, Joseph Gonzalez, Aapo Kyrola, Danny Bickson, Carlos Guestrin. GraphLab: A New Framework for Parallel Machine Learning. Conference on Uncertainty in Artificial Intelligence (UAI), 2010 [2] Kilian Weinberger, Anirban Dasgupta, John Langford, Alex Smola, and Josh Attenberg. 2009. Feature hashing for large scale multitask learning. In Proceedings of the 26th Annual International Conference on Machine Learning (ICML '09). ACM, New York, NY, USA, 1113-1120. [3] Kopytov, Alexey. sysbench. http://sysbench.sourceforge.net/ [4] Linux Kernel Organization. Perf Wiki. https://perf.wiki.kernel.org/index.php/Main_Page Scheduling Consistency Model Data Graph Update Functions Shared Data Table