We design an efficient DMA controller for scientific computing accelerators. It supports several flexible and powerful transfers, including reshape transfers, parameter linking mechanism, and transfer chaining meachnism. We also optimize the DMA controller for critical scientific computing kernels. It supports high bandwidth matrix transposition during data movement. It improves the memory access efficiency for matrix multiplication. Experimental results show that the data movement bandwidth achieved by the DMA controller is similar to the theoretical maximum one. It also performs very closely to an ideal design for real applications.

In deep nanometer scale, Network-on-Chip (NoC) links are more prone to multi-bit transient fault. Conventional ECC techniques brings heavy area, power, and timing overheads when correcting and detecting multiple transient faults. Therefore, a cost-effective ECC technique, named 2D fault coding method, is adopted to overcome the multi-bit transient fault issue of NoC links. Its key innovation is that the wires of a link are treated as its matrix appearance and light-weight Parity Check Coding (PCC) is performed on the matrix's two dimensions (horizontal matrix rows and vertical matrix columns). Horizontal PCCs and vertical PCCs work together to find the faults' position and then correct them by simply inverting them. The procedure of using the 2D fault coding method to protect a NoC link is proposed, its correction and detection capability is analyzed, and its hardware implementation is carried out. Comparative experiments show that the proposal can largely reduce the ECC hardware cost, have much higher fault detection coverage, maintain almost zero silent fault percentages, and have higher fault correction percentages normalized under the same area, demonstrating that it is cost-effective and suitable to the multi-bit transient fault control for NoC links.

This letter proposes a novel classification approach for a hyperspectral image (HSI) using a hierarchical local-receptive-field (LRF)-based extreme learning machine (ELM). As a fast and accurate pattern classification algorithm, ELM has been applied in numerous fields, including the HSI classification. The LRF concept originates from research in neuroscience. Considering the local correlations of spectral features, it is promising to improve the performance of HSI classification by introducing the LRFs. Recent research on deep learning has shown that hierarchical architectures with more layers can potentially extract abstract representation and invariant features for better classification performance. Therefore, we further extend the LRF-based ELM method to a hierarchical model for HSI classification. Experimental results on two widely used real hyperspectral data sets confirm the effectiveness of the proposed HSI classification approach.

SUMMARY Gadget is a simulation application for N‐body and smoothed particle hydrodynamics problems in cosmology, and it is widely applied in solving series of cosmological problems. N‐body focuses on the motion of the interaction of N particles, and smoothed particle hydrodynamics is a fluid simulation algorithm that studies the movement of fluid through particle simulation. Most scholars focus their attention on accelerating Gadget on multi‐core CPU or graphics processing units (GPUs) platforms. However, these research activities failed to achieve CPU–GPU hybrid computing, which resulted in tremendous waste of CPU computing resources. In this paper, we propose a CPU–GPU hybrid parallel strategy to accelerate Gadget‐2, a massively parallel structure formation code for cosmological simulations. This strategy uses CPU and GPU to process the calculation of short‐range force. To ensure CPU and GPU workload balance, a dynamic task allocation scheme is proposed according to the computational performance difference between the CPU and GPU. Experimental results showed that our CPU–GPU hybrid parallel strategy achieved an overall speedup factor of 18.6 and a partial speedup factor for short‐range force calculation of 28.35 compared with a single‐core CPU implementation for particles in million‐size magnitudes. Moreover, compared with a GPU platform that contained 12 CPU cores and one GPU, our hybrid parallel strategy obtained overall speedup and partial speedup factors of 6% and 20%, respectively. Furthermore, the scalability of the hybrid strategy is very fine – its performance will be enhanced when the problem scale is increasing. However, this strategy also has its limitation that the performance enhancement will be decreasing if the ratio(the number of CPU cores divides that of the GPU cards) reduces. Finally, in our hybrid strategy, the CPU coefficient of utilization improved by 17.14% or better. Copyright © 2013 John Wiley & Sons, Ltd.

In the current article, the capability and flexibility of field programmable gate-arrays (FPGAs) to implement IEEE-754 double-precision floating-point elementary functions are explored. To perform various elementary functions on the unified hardware efficiently, we propose a special-purpose very long instruction word (VLIW) processor, called DP_VELP. This processor is equipped with multiple basic units, and its performance is improved through an explicitly parallel technique. Pipelined evaluation of polynomial approximation with Estrin's scheme is proposed, by scheduling basic components in an optimal order to avoid data hazard stalls and achieve minimal latency. The custom VLIW processor can achieve high scalability. Under the control of specific VLIW instructions, the basic units are combined into special-purpose hardware for elementary functions. Common elementary functions are presented as examples to illustrate the design of elementary function in DP_VELP in detail. Minimax approximation scheme is used to reduce degree of polynomial. Compromise between the size of lookup table and the latency is discussed, and the internal precision is carefully planned to guarantee accuracy of the result. Finally, we create a prototype of the DP_VELP unit and an FPGA accelerator based on the DP_VELP unit on a Xilinx XC6VLX760 FPGA chip to implement the SGP4/SDP4 application. Compared with previous researches, the proposed design can achieve low latency with a reasonable amount of resources and evaluate a variety of elementary functions with the unified hardware to satisfy the demands in scientific applications. Experimental results show that the proposed design guarantees more than 99% of correct rounding. Moreover, the SGP4/SDP4 accelerator, which is equipped with 39 DP_VELP units and runs at 200 MHz, outperforms the parallel software approach with hyper-thread technology on an Intel Xeon Quad E5620 CPU at 2.40 GHz by a factor of 7X.

In this article, a unified VLIW coprocessor, based on a common group of atomic operation units, for Quad arithmetic and elementary functions (QP_VELP) is presented. The explicitly parallel scheme of VLIW instruction and Estrin's evaluation scheme for polynomials are used to improve the performance. A two-level VLIW instruction RAM scheme is introduced to achieve high scalability and customizability, even for more complex key program kernels. Finally, the Quad arithmetic accelerator (QAA) with the QP_VELP array is implemented on ASIC. Compared with hyper-thread software implementation on an Intel Xeon E5620, QAA with 8 QP_VELP units achieves improvement by a factor of 18X.

Many scientific computing applications require efficient variable-precision floating-point arithmetic. This paper presents a special-purpose Variable-Precision Floating-Point Arithmetic Processor (VPFPAP) based on Very Large Instruction Word (VLIW) structure. The proposed processor uses a unified hardware structure, equipped with multiple custom basic variable-precision arithmetic units, to implement various variable-precision algebraic and transcendental functions. The performance is improved by the explicitly parallel technology of VLIW instruction and by dynamically varying the precision of intermediate computation. Finally, we create a prototype of VPFPAP unit into a Xilinx Virtex-6 XC6VLX760-2FF1760 FPGA chip. The experimental results show that our design, based on FPGA running at 253 MHz, outperforms the approach of a software-based library running on an Intel Core i3 530 CPU at 2.93 GHz by a factor of 5-38X for basic variable precision arithmetic operations and elementary functions.

This work explores the feasibility to implement IEEE-754-2008 standard quadruple precision (Quad) elementary functions on recent FPGAs with plenty of embedded memories and DSP blocks. First, we analysis the implementation algorithm of Quad elementary functions in detail. Then, we present a special-purpose Very Large Instruction Word (VLIW) architecture for Quad elementary function (QE-Processor). The proposed processor uses a unified hardware structure, equipped with multiple basic arithmetic units, to implement various Quad algebraic and transcendental functions, in which several tradeoffs between latency and resource usage are carefully planned to avoid unbalanced resource utilization. The performance is improved through the explicitly parallel technology of custom VLIW instruction. Finally, we create a prototype of QE-Processor into Xilinx Virtex-5 and Virtex-6 FPGA chips. The experimental results show that our design can guarantee that the percentage of correct rounding is more than 99.9%. Moreover, the FPGA implementation on Virtex-6 XC6VLX760-2FF1760 FPGA, running at 220 MHz, outperforms the parallel software approach based on OpenMP running on an Intel Xeon E5620 CPU at 2.40GHz by a factor of 13X-20X for special function applications in Boost library.

In this paper we explore the capability and flexibility of FPGA solutions in a sense to accelerate scientific computing applications which require very high precision arithmetic, based on 128-bit or even 256-bit floating-point number representation.

Previous works have projected that the peak performance of FPGAs can outperform that of the general purpose processors. However, no work actually compares the performance between FPGAs and CPUs using the standard benchmarks such as the LINPACK benchmark. We propose and implement an FPGA-based hardware design of the LINPACK benchmark, the key step of which is LU decomposition with pivoting. We introduce a fine-grained pipelined LU decomposition algorithm that enables optimum performance by exploiting fine-grained pipeline parallelism. A scalable linear array of processing elements (PEs), which is the core component of our hardware design, is proposed to implement this algorithm. To the best of our knowledge, this is the first reported FPGA-based pipelined implementation of LU decomposition with pivoting. A total of 19 PEs can be integrated into an Altera Stratix II EP2S130F1020C5 on our self-designed development board. Experimental results show that the speedup up to 6.14 can be achieved relative to a Pentium 4 processor for the LINPACK benchmark.

Many FPGA implementations for QR decomposition have been studied on small-scale matrix and all of them are presented individually. However to the best of our knowledge, there is no FPGA-based accelerator for large-scale QR decomposition. In this paper, we propose a unified FPGA accelerator structure for large-scale QR decomposition. To exploit the computational potential of FPGA, we introduce a fine-grained parallel algorithm for QR decomposition. A scalable linear array processing elements (PEs), which is the core component of the FPGA accelerator, is proposed to implement this algorithm. A total of 15 PEs can integrated into an Altera StratixII EP2S130F1020C5 on our self-designed board. Experimental results show that a factor of 4 speedup and the maximum powerperformance of 60.9 can be achieved compare to Pentium Dual CPU with double SSE thread.

FPGA chips have become a promising option for accelerating scientific applications, which involve many floating-point transcendental functions, such as sin, log, exp, sqrt and etc. In this paper, we present a 64-bit ANSI/IEEE floating-point CORDIC co-processor on FPGA, providing all known CORDIC functions. And there is no 64-bit CORDIC implementation on FPGA known to us. We propose a hybrid-mode CORDIC algorithm, combining hybrid rotation angle methods with argument reduction algorithm to reduce hardware area usage and meanwhile keep unlimited convergence domain for any floating-point inputs of the functions. Our hybrid-mode CORDIC co-processor is organized into three phases, argument reduction, CORDIC calculation and normalization with 69 pipeline stages for FPGA implementation. The synthesis results show the clock frequency can reach 173 MHz on Xilinx Virtex5 FPGA. Comparing to general-purpose microprocessor in three scientific program kernels, the CORDIC co-processor can achieve a maximum speedup of 49.3 times, 28.7 times in average.

In the paper, we present a design of FPGA SAR processor with four 1D FFT processing elements, double internal RAM buffers and double external SDRAM modules. Without traditional corner turn phase, we propose a data layout scheme mapping one row of logical matrix into a rectangular window in physical banks of SDRAM in order to increase the practical I/O throughout between SDRAM modules and SAR processing elements. In addition, we theoretically analyses the optimal window size to minimize the total number of opening/closing pages when performing 2D FFT by balancing the number of handling physical pages between row accesses and column accesses. The experimental results show our window layout approach achieves 650 MB/s of effective bandwidth, reaching nearly 82% of peak bandwidth, with 58.1% increases compared to traditional Corner Turn approaches. The proposed SAR processor has been implemented in an FPGA test-bed, outperforming related works in both of computing speed and image scale.