Accelerating Reduction and Scan Using Tensor Core Units

Driven by deep learning, there has been a surge of specialized processors for matrix multiplication, referred to as TensorCore Units (TCUs). These TCUs are capable of performing matrix multiplications on small matrices (usually 4x4 or 16x16) to accelerate the convolutional and recurrent neural networks in deep learning workloads. In this paper we leverage NVIDIA's TCU to express both reduction and scan with matrix multiplication and show the benefits -- in terms of program simplicity, efficiency, and performance. Our algorithm exercises the NVIDIA TCUs which would otherwise be idle, achieves 89%-98% of peak memory copy bandwidth, and is orders of magnitude faster (up to 100x for reduction and 3x for scan) than state-of-the-art methods for small segment sizes -- common in machine learning and scientific applications. Our algorithm achieves this while decreasing the power consumption by up to 22% for reduction and16%for scan.Comment: In Proceedings of the ACM International Conference on Supercomputing (ICS '19

Portable Inter-workgroup Barrier Synchronisation for GPUs

Despite the growing popularity of GPGPU programming, there is not yet a portable and formally-specified barrier that one can use to synchronise across workgroups. Moreover, the occupancy-bound execution model of GPUs breaks assumptions inherent in traditional software execution barriers, exposing them to deadlock. We present an occupancy discovery protocol that dynamically discovers a safe estimate of the occupancy for a given GPU and kernel, allowing for a starvation-free (and hence, deadlock-free) inter-workgroup barrier by restricting the number of workgroups according to this estimate. We implement this idea by adapting an existing, previously non-portable, GPU inter-workgroup barrier to use OpenCL 2.0 atomic operations, and prove that the barrier meets its natural specification in terms of synchronisation. We assess the portability of our approach over eight GPUs spanning four vendors, comparing the performance of our method against alternative methods. Our key findings include: (1) the recall of our discovery protocol is nearly 100%; (2) runtime comparisons vary substantially across GPUs and applications; and (3) our method provides portable and safe inter-workgroup synchronisation across the applications we study

Inter-workgroup barrier synchronisation on graphics processing units

GPUs are parallel devices that are able to run thousands of independent threads concurrently. Traditional GPU programs are data-parallel, requiring little to no communication, i.e. synchronisation, between threads. However, classical concurrency in the context of CPUs often exploits synchronisation idioms that are not supported on GPUs. By studying such idioms on GPUs, with an aim to facilitate them in a portable way, a wider and more generic space of GPU applications can be made possible. While the breadth of this thesis extends to many aspects of GPU systems, the common thread throughout is the global barrier: an execution barrier that synchronises all threads executing a GPU application. The idea of such a barrier might seem straightforward, however this investigation reveals many challenges and insights. In particular, this thesis includes the following studies: Execution models: while a general global barrier can deadlock due to starvation on GPUs, it is shown that the scheduling guarantees of current GPUs can be used to dynamically create an execution environment that allows for a safe and portable global barrier across a subset of the GPU threads. Application optimisations: a set GPU optimisations are examined that are tailored for graph applications, including one optimisation enabled by the global barrier. It is shown that these optimisations can provided substantial performance improvements, e.g. the barrier optimisation achieves over a 10X speedup on AMD and Intel GPUs. The performance portability of these optimisations is investigated, as their utility varies across input, application, and architecture. Multitasking: because many GPUs do not support preemption, long-running GPU compute tasks (e.g. applications that use the global barrier) may block other GPU functions, including graphics. A simple cooperative multitasking scheme is proposed that allows graphics tasks to meet their deadlines with reasonable overheads.Open Acces