We present FusionSim, a modeling framework capable of cycle-accurate simulation of a complete x86-based computer system with (a) a CPU and a GPU on the same die, and (b) a CPU and a GPU connected as separate components. We use FusionSim to characterize the performance of the Rodinia benchmarks on fused and discrete systems. We demonstrate that the speed-up due to fusion is highly correlated with the input data size. We demonstrate that for benchmarks that benefit most from fusion, a 9.72x speed-up is possible for small problem sizes. This speedup reduces to 1.84x with medium problem sizes. We study a software-managed coherence solution for the fused system. We find that it imposes a minor performance overhead of 2% for most benchmarks. Finally, we develop an analytical model for the performance benefit that is to be expected from fusion and show that FusionSim follows the predicted performance trend.

We present FusionSim, a modeling framework capable of cycle-accurate simulation of a complete x86-based computer system with (a) a CPU and a GPU on the same die, and (b) a CPU and a GPU connected as separate components. We use FusionSim to characterize the performance of the Rodinia benchmarks on fused and discrete systems. We demonstrate that the speed-up due to fusion is highly correlated with the input data size. We demonstrate that for benchmarks that benefit most from fusion, a 9.72x speed-up is possible for small problem sizes. This speedup reduces to 1.84x with medium problem sizes. We study a software-managed coherence solution for the fused system. We find that it imposes a minor performance overhead of 2% for most benchmarks. Finally, we develop an analytical model for the performance benefit that is to be expected from fusion and show that FusionSim follows the predicted performance trend.