The emergence of streaming multicore processors with multi-SIMD architectures and ultra-low power operation combined with real-time compute and I/O reconfigurability opens unprecedented opportunities for executing sophisticated signal processing algorithms faster and within a much lower energy budget. Here, we present an unconventional FFT implementation scheme for the IBM Cell, named transverse vectorization. It is shown to outperform (both in terms of timing or GFLOP throughput) the fastest FFT results reported to date in the open literature.
SUMMARYThe emergence of streaming multicore processors with multi-SIMD (single-instruction multiple-data) architectures and ultra-low power operation combined with real-time compute and I/O reconfigurability opens unprecedented opportunities for executing sophisticated signal processing algorithms faster and within a much lower energy budget. Here, we present an unconventional Fast Fourier Transform (FFT) implementation scheme for the IBM Cell, named transverse vectorization. It is shown to outperform (both in terms of timing and GFLOP throughput) the fastest FFT results reported to date for the Cell in the open literature. We also provide the first results for multi-FFT implementation and application on the novel, ultra-low power Coherent Logix HyperX processor.
Single-instruction, multiple-data (SIMD) multicore computing architectures, such as the IBM Cell Broadband Engine Architecture, offer new opportunities for quickly and efficiently calculating the 1D-FFT of acoustic signals, as time-sampled data arrays can be naturally partitioned across the multiple cores on which vectorized implementations of the FFT operate. Building on this parallel pipeline model, we consider the case that M data arrays of length N reside within each core. Whereas the cost of sequentially executing these M FFT's conventionally scales as αMNlog2N, we demonstrate a transverse vectorization solution whose cost scales as αβNlog2N, where α and β are constant scaling factors. Our approach makes use of the SIMD instruction set and large vector register file inherent to each core of the IBM Cell in order to calculate the FFT of M data arrays simultaneously. By efficiently using all the available vector registers in performing the FFT, this transverse SIMD vectorization solution further reduces the computational complexity of the conventional parallel pipeline model.
The design of any new sensing array is a complex endeavor, demanding clear understanding of both signal exploitation options and the underlying noise environment (including ambient, structural, flow and electronic components), including cross-channel characteristics. The goal of this effort is to outline an approach for assessing potential advantages of vector sensors to provide ocean gliders with the acoustic sensory input necessary to execute ISR, characterize the environment, and support other mission-enhancing behaviors. At the heart of the approach is development of a noise audit model (NAM) which (coupled with appropriate signal characterizations) enables realistic and comparable evaluation of optimal processing performance for different classes of sensors (e.g., scalar, vector, or tensor). The NAM is a design tool that permits balanced design of the various array components so that the array output is ambient noise limited in the quietest operating environment. Noise components are broken into different source-transmission path chains; correct transfer functions are applied along each path; and sensor/array outputs are then obtained by incoherent sum. Properly structured, rapid evaluation of performance limits can also be supported. The approach will be discussed, and examples of both NAM components and associated performance evaluations will be presented. [Work supported by ONR.]
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