Graphics Processing Units (GPUs) are rapidly bringing the computing power traditionally associated with massively parallel supercomputers into the mainstream devices we use today. They have the power to revolutionize computing by enabling orders of magnitude faster and more efficient execution of many applications. Unfortunately, many modern applications and users cannot take advantage of the computing capability present in today's GPUs because today's GPUs are used as secondary devices to the much less powerful CPUs. As a result, the massive computing power of GPUs gets wasted and underutilized for a large number of important applications.
This project aims to take a fresh and comprehensive look at GPU design with the goal of enabling GPUs as first-class computing engines that can benefit an overwhelming majority of real-world applications and users. To this end, this project systematically investigates the hardware/software design space of three new execution models, which progressively turn a GPU into an independent, first-class compute engine in a hybrid computing system: 1) an enhanced master-slave model where the GPU is able to perform multiple-application execution, 2) a new peer-to-peer model where the GPU is autonomous of the CPU, 3) a hybrid model where GPUs and CPUs are integrated on the same die and are equals from the applications' and system's viewpoint. The project comprehensively develops software, hardware and software/hardware cooperative scheduling, resource management, and system design techniques for all three models.
If successful, this project can pave the way to making GPUs first-class computing engines used in all aspects of our everyday lives for a majority of applications. Doing so is not only expected to lead to much higher degrees of energy efficiency and user productivity but can also potentially enable new applications and devices that can take advantage GPUs.
