Instrumentation is ubiquitous in computer software today though itsuse in parallel processing frameworks is not widespread. In this thesis,we have developed an instrumentation framework, which we haveintegrated with the P2G framework. The instrumentation frameworkfeeds a high and low level scheduler with detailed instrumentation data,while inducing a minimal of overhead. Our instrumentation frameworkalso collects a wealth of information about the machine it runs on,including capabilities, enabling P2G to support specialized hardware. Todemonstrate the feasibility of our framework, we have run a series oftests that shows promising results, both for the schedulers and developersseeking to locate a performance bottleneck, even though P2G, at the time,was not able to use this data for enhancing the decision making process.