Recent desktop and mobile processors often integrate CPU and GPU onto the same die. The limited memory bandwidth of these integrated architectures can negatively affect the performance of data-parallel workloads when all computational resources are active. The combination of active CPU and GPU cores achieving the maximum performance depends on a workload's characteristics, making manual tuning a time-consuming task. Dopia is a fully automated framework that improves the performance of data-parallel workloads by adjusting the Degree Of Parallelism on Integrated Architectures. Dopia transparently analyzes and rewrites OpenCL kernels before executing them with the number of CPU and GPU cores expected to yield the best performance. Evaluated on AMD and Intel integrated processors, Dopia achieves 84% of the maximum performance attainable by an oracle.

Integrating CPUs and GPUs on the same die provides new opportunities for optimization, especially for irregular data-parallel workloads that fail to fully exploit the computational power of the GPU. Such workloads benefit from a proper partitioning between the CPU and the GPU. This paper presents an on-the-fly workload partitioning technique for irregular workloads on integrated architectures. Unlike existing work, no prior analysis of the workload is required. GPU kernels and input data are analyzed and optimized at runtime. The technique executes work chunks of similar load on the GPU and assigns irregular chunks to the CPU. Evaluated with various irregular workloads, the method achieves a 1.4x--7.1x speedup over GPU execution on AMD and Intel processors.