Workload Mode Identification for Chip Heterogeneous Multiprocessors

With the potential for tens to hundreds of processing elements on future single chip multicore designs also comes the potential to execute a wider variety of input streams, or workloads. At the same time, the trend is for single users to utilize an entire single chip multicore computer. A central challenge for these computers is how to model and identify persistent changes in the input stream, or workload modes. Computer architects often model single program phases as Markov chains. We define workload modes and analyze and evaluate two modeling techniques, a Workload Classification Model (WCM) and a Hidden Markov Model (HMM). We include experimentation on a cell phone example, illustrating how WCM is, on average, 34 times more time efficient and 83% more space efficient than HMM, while improving overall performance by an average of 191% and being, on average, 56% more energy efficient. We found that even sub-optimal use of WCM can outperform HMM, further supporting the need for design time workload models. Our main contribution is to show how the design of single user multicore architecture to models of workloads that arise from single user usage patterns will be necessary as the complexity of applications and architectures grows. Thus, we advocate Workload Specific Processors as a new means of orienting single-user chip heterogeneous multiprocessors.