Blake A. Hechtman

QuickRelease: A throughput-oriented approach to release consistency on GPUs

Graphics processing units (GPUs) have specialized throughput-oriented memory systems optimized for streaming writes with scratchpad memories to capture locality explicitly. Expanding the utility of GPUs beyond graphics encourages designs that simplify programming (e.g., using caches instead of scratchpads) and better support irregular applications with finer-grain synchronization. Our hypothesis is that, like CPUs, GPUs will benefit from caches and coherence, but that CPU-style “read for ownership” (RFO) coherence is inappropriate to maintain support for regular streaming workloads. This paper proposes QuickRelease (QR), which improves on conventional GPU memory systems in two ways. First, QR uses a FIFO to enforce the partial order of writes so that synchronization operations can complete without frequent cache flushes. Thus, non-synchronizing threads in QR can re-use cached data even when other threads are performing synchronization. Second, QR partitions the resources required by reads and writes to reduce the penalty of writes on read performance. Simulation results across a wide variety of general-purpose GPU workloads show that QR achieves a 7% average performance improvement compared to a conventional GPU memory system. Furthermore, for emerging workloads with finer-grain synchronization, QR achieves up to 42% performance improvement compared to a conventional GPU memory system without the scalability challenges of RFO coherence. To this end, QR provides a throughput-oriented solution to provide fine-grain synchronization on GPUs.

Exploring memory consistency for massively-threaded throughput-oriented processors

We re-visit the issue of hardware consistency models in the new context of massively-threaded throughput-oriented processors (MTTOPs). A prominent example of an MTTOP is a GPGPU, but other examples include Intels MIC architecture and some recent academic designs. MTTOPs differ from CPUs in many significant ways, including their ability to tolerate latency, their memory system organization, and the characteristics of the software they run. We compare implementations of various hardware consistency models for MTTOPs in terms of performance, energy-efficiency, hardware complexity, and programmability. Our results show that the choice of hardware consistency model has a surprisingly minimal impact on performance and thus the decision should be based on hardware complexity, energy-efficiency, and programmability. For many MTTOPs, it is likely that even a simple implementation of sequential consistency is attractive.

Evaluating cache coherent shared virtual memory for heterogeneous multicore chips

Although current homogeneous chips tightly couple the cores with cache-coherent shared virtual memory (CCSVM), this is not the communication paradigm used by any current heterogeneous chip. In this paper, we present a CCSVM design for a CPU/GPU chip, as well as an extension of the pthreads programming model for programming this HMC. We experimentally compare CCSVM/xthreads to a state-of-the-art CPU/GPU chip from AMD that runs OpenCL software. CCSVMs more efficient communication enables far better performance and far fewer DRAM accesses.