Efficient simulation techniques for large-scale applications

Abstract

Architecture simulation is an important performance modeling approach. Modeling hardware components with sufficient detail helps architects to identify both hardware and software bottlenecks. However, the major issue of architectural simulation is the huge slowdown compared to native execution. The slowdown gets higher for the emerging workloads that feature high throughput and massive parallelism, such as GPGPU kernels. In this dissertation, three simulation techniques were proposed to simulate emerging GPGPU kernels and data analytic workloads efficiently. First, TBPoint reduce the simulated instructions of GPGPU kernels using the inter-launch and intra-launch sampling approaches. Second, GPUmech improves the simulation speed of GPGPU kernels by abstracting the simulation model using functional simulation and analytical modeling. Finally, SimProf applies stratified random sampling with performance counters to select representative simulation points for data analytic workloads to deal with data-dependent performance. This dissertation presents the techniques that can be used to simulate the emerging large-scale workloads accurately and efficiently.