In this paper we introduce a fast discrete event-driven simulation methodology, called KnightSim, that is intended for use in the development of future computer architectural simulations. KnightSim extends an older event-driven simulation library by (1) incorporating corrections to functional issues that were introduced by the recent additions of stack protection, pointer mangling, and source fortification in the Linux software stack, (2) incorporating optimizations to the event engine, and (3) introducing a novel parallel implementation. KnightSim implements events as independently executable x86 “KnightSim Contexts”. KnightSim Contexts comprise a mechanism for fast context execution and automatically model occupancy and contention, which readily lends itself to use in computer architectural simulations. We present the implementation methodologies of KnightSim and Parallel KnightSim with a detailed performance analysis. Our performance analysis makes direct comparisons between KnightSim, Parallel KnightSim, and the discrete event-driven simulation engines found in three different mainstream computer architectural simulators. Our results show that on average KnightSim achieves speedups of 2.8 to 11.9 over the other discrete event-driven simulation engines. Our results also show that on average Parallel KnightSim can achieve speedups over KnightSim of 1.89, 3.33, 5.84, and 9.24 for 2, 4, 8, and 16 threaded executions respectively.

We introduce M2S-CGM a detailed architectural simulator that models the interactions between CPUs and GPUs operating in coherent heterogeneous compute environments. M2S-CGM extends an existing and established x86 CPU model and Southern Islands GPU model, adds a new custom-built memory system model and switching fabric called CGM, and incorporates a well-known SDRAM model. The CGM memory system simulator provides configurable entire system simulation and can support a range of non-coherent and coherent CPU-GPU configurations. M2S-CGM supports the runtime for OpenCL-based benchmarks in addition to traditional multithreaded CPU benchmarks and can run benchmarks from established heterogeneous benchmark collections. This allows us to experiment with different coherent CPU-GPU configurations and propose effective future improvements in these systems. We present the makeup of M2S-CGM's software architectural design, provide a validation of the simulator, and provide coherent CPU-GPU execution results. Our validation results show average differences between our physical test system and M2S-CGM, of 10.4%, 22%, and 6.4% for 2 threaded, 4 threaded, and heterogeneous benchmark runs respectively. Our coherent CPU-GPU experimental results show an average speedup of 2.8 for our benchmarks over the baseline noncoherent system.