Performance comparison of single-precision SPICE Model-Evaluation on FPGA, GPU, Cell, and multi-core processors

Abstract

Automated code generation and performance tuning techniques for concurrent architectures such as GPUs, Cell and FPGAs can provide integer factor speedups over multi-core processor organizations for data-parallel, floating-point computation in SPICE Model-Evaluation. Our Verilog AMS compiler produces code for parallel evaluation of non-linear circuit models suitable for use in SPICE simulations where the same model is evaluated several times for all the devices in the circuit. Our compiler uses architecture specific parallelization strategies (OpenMP for multi-core, PThreads for Cell, CUDA for GPU, statically scheduled VLIW for FPGA) when producing code for these different architectures. We automatically explore different implementation configurations (e.g. unroll factor, vector length) using our performance-tuner to identify the best possible configuration for each architecture. We demonstrate speedups of 3-182× for a Xilinx Virtex5 LX 330T, 1.3-33× for an IBM Cell, and 3-131× for an NVIDIA 9600 GT GPU over a 3 GHz Intel Xeon 5160 implementation for a variety of singleprecision device models.

Reconfigurable Computing Architectures

“…For compiled CUDA, the native language of many GPGPUs, the GPGPUs provide 1-4Â higher throughput than FPGAs, but at a cost of over 4-16Â the energy per operation [60]. The peak, single-precision, floating-point throughput of GPGPUs exceeds processors and FPGAs, but the delivered performance on applications can be lower than FPGAs and the energy per operation can be higher [61]. FPGAs can outperform GPGPUs [62] for video processing, depending on the nature and complexity of the task.…”

Comparison of processing performance and architectural efficiency metrics for FPGAs and GPUs in 3D Ultrasound Computer Tomography

“…For a meaningful comparison performance values would need to be normalized to a common denominator in this case. The same is true in [7]- [9], where the target devices are of the same generation, but of different grade, i.e. not the fastest, and in case of the FPGA also largest, available devices are chosen.…”

Sparse LU factorization for parallel circuit simulation on GPU

“…In the last decade, Graphic Processing Units (GPU) prove useful in many fields [2]. There have been works on GPU-based model evaluation [3,4]. The parallelization of model evaluation in SPICE simulation is straightforward since it consists of a large amount of independent tasks, i.e.…”