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 benefits 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 communication between CPU and GPU by building user-level interrupts on top of existing cache coherency mechanisms. By removing graphics-specific hardware on a 65 nm process, the area saved is equivalent to 9 GPU cores, while the power saved is equivalent to 5 cores. Our FPGA prototype shows thread spawn latency improvements from thousands of clock cycles to 26. A set of non-graphics workloads demonstrate speedups of up to 8.8x. This thesis also presents a limit study, where we measure the limit of algorithm parallelism in the context of a heterogeneous system that can be usefully extracted from a set of general-purpose applications. We measure sensitivity to the sequential performance (register read-after-write latency) of the low-cost parallel cores, and latency and bandwidth of the communication channel between the two cores. Using these measurements, we propose system characteristics that maximize area and power efficiencies. As in previous limit studies, we find a high amount of parallelism. We show, however, that the potential speedup on GPU-like systems is low (2.2x - 12.7x) due to poor sequential performance. Communication latency and bandwidth have comparatively small performance effects (<25%). Optimal area efficiency requires a lower-cost parallel processor while optimal power efficiency requires a higher-performance parallel processor than today's GPUs.

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