An Efficient Analysis Approach for Multi-Core System Tracing Data

Abstract

Trace data usually contains information about the underlying system execution such as running processes, memory usage, disk and file accesses and other runtime information. However, this raw data is not what the system administrators are looking for. For instance, in a multicore system, the system administrator may need to know what is the utilization of each core or even what are the performance bottlenecks of the system. This analytical information is not available directly from the trace data and, in fact, is hidden behind the mountains of trace raw data. The trace data needs to be analyzed to extract the valuable information. Thus, efficient trace analysis tools and techniques need to be developed to handle large trace data and extract and provide useful and analytical information. In this paper, we propose a stateful trace analysis and abstrac tion approach that shares the computation and storage of the common information between parallel abstraction processes. This technique leads to relatively simple patterns compared to other pattern-based techniques. Furthermore, since it computes and stores the common state once, shared between related processes, it has a better computation and storage efficiency than stateless methods. The architectur e of the method, its applications, and evaluation are detaile d in this paper.