Embedded Software Engineering Approach to Implement BCM5354 Processor Performance

Efficiency of a processor is a critical factor for an embedded system. One of the deciding factors for efficiency is the functioning of the L1 cache and Translation Lookaside Buffer (TLB). Certain processors have the L1 cache and TLB managed by the operating system, MIPS32 is one such processor. The performance of the L1 cache and TLB necessitates a detailed study to understand its management during varied load on the processor. This paper presents an implementation to analyse the performance of the MIPS32 processor L1 cache and TLB management by the operating system (OS) using software engineering approach. Software engineering providing better clearity for the system developemt and its performance analysis.In the initial stage if the requirement analysis for the performance measurment sort very clearly,the methodologies for the implementation becomes very economical without any ambigunity.In this paper a implementation is proposed to determine the processor performance metrics using a software engineering approach considering the counting of the respective cache and TLB management instruction execution, which is an event that is measurable with the use of dedicated counters. The lack of hardware counters in the MIPS32 processor results in the usage of software based event counters that are defined in the kernel. This paper implements a subset of MIPS32 processor performance measurement metrics using software based counters. Techniques were developed to overcome the challenges posed by the kernel source code. To facilitate better understanding of the implementation procedure of the software based processor performance counters; use-case analysis diagram, flow charts, screen shots, and knowledge nuggets are supplemented along with histograms of the cache and TLB events data generated by the proposed implementation. Twenty-seven metrics have been identified and implemented to provide data related to the events of the L1 cache and TLB on the MIPS32 processor. The generated data can be used in tuning of compiler, OS memory management design, system benchmarking, scalability, analysing architectural issues, address space analysis, understanding bus communication, kernel profiling, and workload characterisation