Architectures and Limits of GPU-CPU Heterogeneous Systems

As we continue to be able to put an increasing number of transistors on a single chip, the answer to the perpetual question of what the best processor we could build with the transistors is remains uncertain. Past work has shown that heterogeneous multiprocessor systems provide benefits in performance and efficiency. This thesis explores heterogeneous systems composed of a traditional sequential processor (CPU) and highly parallel graphics processors (GPU). This thesis presents a tightly-coupled heterogeneous chip multiprocessor architecture for general-purpose non-graphics computation and a limit study exploring the potential ben-efits of GPU-like cores for accelerating a set of general-purpose workloads. Pangaea is a heterogeneous CMP design for non-rendering workloads that integrates IA32 CPU cores with GMA X4500 GPU cores. Pangaea introduces a resource partitioning of the GPU, where 3D graphics-specific hardware is removed to reduce area or add more processing cores, and a 3-instruction extension to the IA32 ISA that supports fast commu-nication between CPU and GPU by building user-level interrupts on top of existing cach