Ideas on improving Linux infrastructure for performance on multi-core platforms

With maturing compiler technologies, compile-time analysis can be a very powerful tool for optimizing and monitoring code on any architecture. In combination with modern runtime analysis tools and existing program interfaces to monitor hardware counters, we will survey modern techniques for analyzing performance issues. We propose using performance counter data and sequences of performance events to trigger event handlers in either the application or the operating system. In this way, sequence of performance events can be your debugging breakpoint or a callback. This paper will try to bridge the capabilities of advanced performance monitoring with common software development infrastructure (debuggers, gcc, loader, process scheduler). One proposed approach is to extend the run-time environment with an interface layer that will filter performance profiles, capture sequences of performance hazards, and provide summary data to the OS, debuggers, or application.

With the introduction of hyper-threading technology several years ago, there were obvious challenges to look beyond a single running process to monitor and schedule compute intensive processes on multi-threaded cores. Multi-level memory hierarchy and scaling on SMP systems complicated the situation even further, causing essential changes in kernel scheduler and performance tools. In the era of parallel and platform computing, we rely less on single execution process performance - with each component optimized by the compiler - and it becomes important to evaluate performance of the platform as a whole. The new concept of performance adaptive schedulers is one example of intelligently maximizing the performance on platform level of CMP systems. Performance data at higher granularity and a concept of processor efficiency per functionality can be applied to making intelligent decisions on process scheduling in the operating system.

Towards the end, we will suggest particular improvements in performance and run-time tools as a reflection of proposed approaches and transition to platform-level optimization goals.


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