Hardware accelerators such as Graphics Processing Units (GPUs), Intel Xeon Phi co-processors (PHIs), and Field-Programmable Gate Arrays (FPGAs) are now ubiquitous in extreme-scale high performance computing (HPC), cloud, and Big data platforms to facilitate execution of workloads that demand high energy efficiency. They present unique interfaces and programming models therefore posing several limitations, which must be addressed to facilitate execution of large workloads. There is no library providing a unifying interface that allows programmers to write reusable out-of-core implementations of their data-parallel kernels that can run efficiently on different mainstream accelerators such as GPUs, PHIs, and FPGAs. We address this shortage in this paper. We present a library called libhclooc, which provides a unifying interface facilitating out-of-core implementations for data parallel kernels on the three different mainstream accelerators (GPUs, Intel Xeon Phis, FPGAs). We implement out-of-core matrix-matrix multiplication (MMOOC) using the libhclooc API and demonstrate its superior performance over vendor implementations. We show that it suffers from a maximum overhead of 10 , 4 , and 8 (due to abstraction) compared to the state-of-the-art optimised implementations for Nvidia K40c GPU, Nvidia P100 PCIe GPU, and Intel Xeon Phi 3120P respectively. We also show that using libhclooc API reduces the number of lines of code (LOC) by 75 thereby drastically improving programmer productivity.
@cite_17 present an out-of-core implementation of FFT kernel for a single GPU where they overlap kernel computations on the GPU and communications over the PCI-E bus. @cite_14 propose an out-of-core algorithm for LU decomposition.
@cite_14 @cite_17
2328600717 2074570088
In this paper, a full realization of the higher order method of moments (HMoM) with a parallel out-of-core LU solver on GPU CPU platform is presented in detail, mainly including three parts: In the first part, both global-auxiliary table and local-auxiliary table are introduced for reducing a lot of tedious and repetitive calculations, and then a realization for GPU-oriented programming is proposed and optimized. In the second part, an overlapped grouping of all the curved quadrilaterals is proposed. With this scheme, all the submatrices can be efficiently generated one by one without wasting any calculations with the help of both the video memory and the host memory. In the third part, a GPU-based out-of-core algorithm for LU decomposition is proposed and further developed into a hybrid GPU CPU algorithm. Numerical examples are provided to test the robustness of the proposed algorithm by comparison with the measurement and or the traditional MoM with RWG basis functions, and to demonstrate the overall performance of the proposed algorithm by comparison with the existing algorithm for dealing with similar problems. The speedup ratio of the proposed algorithm for generating the HMoM matrix can achieve about from 7 to 12 compared with the GPU-based algorithm in literatures. Also compared with the 8-threaded CPU-based algorithm, the speedup ratio of the proposed algorithm for LU decomposition can exceed 13 for the single precision case and 7 for the double precision case. The optimization of Fast Fourier Transfer (FFT) problems that can fit into GPU memory has been studied extensively. Such on-card FFT libraries like CUFFT can generally achieve much better performance than their counterparts on a CPU, as the data transfer between CPU and GPU is usually not counted in their performance. This high performance, however, is limited by the GPU memory size. When the FFT problem size increases, the data transfer between system and GPU memory can comprise a substantial part of the overall execution time. Therefore, optimizations for FFT problems that outgrow the GPU memory can not bypass the tuning of data transfer between CPU and GPU. However, no prior study has attacked this problem. This paper is the first effort of using GPUs to efficiently compute large FFTs in the CPU memory of a single compute node. In this paper, the performance of the PCI bus during the transfer of a batch of FFT subarrays is studied and a blocked buffer algorithm is proposed to improve the effective bandwidth. More importantly, several FFT decomposition algorithms are proposed so as to increase the data locality, further improve the PCI bus efficiency and balance computation between kernels. By integrating the above two methods, we demonstrate an out-of-card FFT optimization strategy and develop an FFT library that efficiently computes large 1D, 2D and 3D FFTs that can not fit into the GPU's memory. On three of the latest GPUs, our large FFT library achieves much better double precision performance than two of the most efficient CPU based libraries, FFTW and Intel MKL. On average, our large FFTs on a single GeForce GTX480 are 46 faster than FFTW and 57 faster than MKL with multiple threads running on a four-core Intel i7 CPU. The speedup on a Tesla C2070 is 1.93x and 2.11x over FFTW and MKL. A peak performance of 21GFLOPS is achieved for a 2D FFT of size 2048x65536 on C2070 with double precision.