A Data-Centric Directive-Based Framework to Accelerate Out-of-Core Stencil Computation on a GPU

Abstract

Graphics processing units (GPUs) are highly efficient architectures for parallel stencil code; however, the small device (i.e., GPU) memory capacity (several tens of GBs) necessitates the use of out-of-core computation to process excess data. Great programming effort is needed to manually implement efficient out-of-core stencil code. To relieve such programming burdens, directive-based frameworks emerged, such as the pipelined accelerator (PACC); however, they usually lack specific optimizations to reduce data transfer. In this paper, we extend PACC with two data-centric optimizations to address data transfer problems. The first is a direct-mapping scheme that eliminates host (i.e., CPU) buffers, which intermediate between the original data and device buffers. The second is a region-sharing scheme that significantly reduces host-to-device data transfer. The extended PACC was applied to an acoustic wave propagator, automatically extending the length of original serial code 2.3-fold to obtain the out-of-core code. Experimental results revealed that on a Tesla V100 GPU, the generated code ran 41.0, 22.1, and 3.6 times as fast as implementations based on Open Multi-Processing (OpenMP), Unified Memory, and the previous PACC, respectively. The generated code also demonstrated usefulness with small datasets that fit in the device capacity, running 1.3 times as fast as an in-core implementation.

Accelerating GPU-Based Out-of-Core Stencil Computation with On-the-Fly Compression

“…In this section, we analyze the experimental results to evaluate the benefits of using on-the-fly compression in out-of-core stencil computation on a GPU. The stencil code we used is an acoustic wave propagator from a previous work [3] of ours. The code is a 25-point stencil computation that has two read-write datasets, a writeonly dataset, and a read-only dataset.…”

“…Stencil computation is an important computation paradigm that appears in many scientific applications, such as geophysics simulations [1]- [3], computational electromagnetics [4], and image processing [5]. The key principle of stencil computation is to iteratively apply a fixed calculation pattern (stencil, where the update of an element relies on the surrounding elements) to every element of the output datasets.…”

“…A large entity of research on GPU-based out-of-core stencil computation has been performed to address this issue [3], [10]- [13]. For a large dataset whose data size exceeds the capacity of the device memory, out-of-core computation first decomposes the dataset into smaller blocks and then streams the blocks to and from the GPU for processing.…”

“…Data-centric strategies are thus necessary to reduce the data transfer between the CPU and GPU. Studies have introduced strategies such as temporal blocking and region sharing to reuse the on-GPU data and to avoid transferring extra data [3], [10], [13]. Nevertheless, we need to further optimize the methods to reduce data transfer time.…”

“…Nevertheless, we need to further optimize the methods to reduce data transfer time. according to [3], the performance of out-of-core stencil code is still limited by data transfer. A potential solution is to use on-the-fly compression to compress the data on the GPU before transferring back to the CPU, and decompress the data on the GPU before processing.…”