Identifying On-/Off-CPU Bottlenecks Together with Blocked Samples

The rapid advancement of computer system components has necessitated a comprehensive profiling approach for both on-CPU and off-CPU events simultaneously. However, the conventional approach lacks profiling both on-and off-CPU events, so they fall short of accurately assessing the overhead of each bottleneck in modern applications.

In this paper, we propose a sampling-based profiling technique called blocked samples that is designed to capture all types of off-CPU events, such as I/O waiting, blocking synchronization, and waiting in CPU runqueue. Using the blocked samples technique, this paper proposes two profilers, bperf and BCOZ. Leveraging blocked samples, bperf profiles applications by providing symbol-level profile information when a thread is either on the CPU or off the CPU, awaiting scheduling or I/O requests. Using the information, BCOZ performs causality analysis of collected on-and off-CPU events to precisely identify performance bottlenecks and the potential impact of optimizations. The profiling capability of BCOZ is verified using real applications. From our profiling results followed by actual optimization, BCOZ identifies bottlenecks with off-CPU events precisely, and their optimization results are aligned with the predicted performance improvement by BCOZ's causality analysis.