Computer Generation of Efficient Software Viterbi Decoders

Mesmay, Frédéric de; Chellappa, Srinivas; Franchetti, Franz; Püschel, Markus

doi:10.1007/978-3-642-11515-8_26

Cited by 10 publications

(9 citation statements)

References 17 publications

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“…Recently, there were several works about the VDA on different platforms, including CPUs , GPUs , and FPGAs . However, there were only few works centralizing on the TVDA .…”

Section: Introductionmentioning

confidence: 99%

“…In addition, we also reveal the efforts that programmers have made during performance tuning processes. After the investigation of efficient parallel algorithms for Viterbi decoding on three platforms, we also evaluate and compare the performance and efficiency to discuss the suitable flexible architecture for real-time Viterbi decoding.Recently, there were several works about the VDA on different platforms, including CPUs [10,11], GPUs [7,8,12,13], and FPGAs [14][15][16][17][18][19][20][21]. However, there were only few works centralizing on the TVDA [16][17][18][19][20][21].…”

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confidence: 99%

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Efficient parallel implementation of three‐point viterbi decoding algorithm on CPU, GPU, and FPGA

Dou

Zou

2013

Concurrency and Computation

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SUMMARYIn wireless communication, Viterbi decoding algorithm (VDA) is the one of most popular channel decoding algorithms, which is widely used in WLAN, WiMAX, or 3G communications. However, the throughput of Viterbi decoder is constrained by the convolutional characteristic. Recently, the three‐point VDA (TVDA) was proposed to solve this problem. In TVDA, the whole procedure can be divided into three phases, the forward, trace‐back, and decoding phases. In this paper, we analyze the parallelism of TVDA and propose parallel TVDA on the multi‐core CPU, graphics processing unit (GPU), and field programmable gate array (FPGA). We demonstrate approaches that fully exploit its performance potential on CPU, GPU, and FPGA computing platforms. For CPU platforms, we perform two optimization methods, single instruction multiple data and multithreading to gain over 145 × speedup over the naive CPU version on a quad‐core CPU platform. For GPU platforms, we propose the combination of cached memory optimization, coalesced global memory accesses, codeword packing scheme, and asynchronous data transition, achieving the throughput of 404.65 Mbps and 12 × speedup over initial GPU versions on an NVIDIA GeForce GTX580 card and 7 × speedup over Intel quad‐core CPU i5‐2300, under the same manufacturing year and both with fully optimized schemes. In addition, for FPGA platforms, we customize a radix‐4 pipelined architecture for the TVDA in a 45‐nm FPGA chip from Xilinx (XC6VLX760). Under 209.15‐MHz clock rate, it achieves a throughput of 418.30 Mbps. Finally, we also discuss the performance evaluation and efficiency comparison of different flexible architectures for real‐time Viterbi decoding in terms of the decoding throughput, power consumption, optimization schemes, programming costs, and price costs.Copyright © 2013 John Wiley & Sons, Ltd.

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“…Recently, there were several works about the VDA on different platforms, including CPUs , GPUs , and FPGAs . However, there were only few works centralizing on the TVDA .…”

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

Efficient parallel implementation of three‐point viterbi decoding algorithm on CPU, GPU, and FPGA

Dou

Zou

2013

Concurrency and Computation

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“…So far, the problems targeted by SPIRAL have been in the domain of digital signal processing algorithms, eg, linear transforms like the ubiquitous discrete Fourier transform (DFT), filters, the wavelet transform, and other digital signal processing kernels such as Viterbi decoders, the image formation algorithm in synthetic aperture radar, space‐time adaptive processing, and components of JPEG 2000. A further domain explored is basic linear algebra .…”

Section: Spiralmentioning

confidence: 99%

Algebraic description and automatic generation of multigrid methods in SPIRAL

Bolten

Franchetti

Lengauer

et al. 2017

Concurrency and Computation

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SPIRAL is an autotuning, program generation and code synthesis system that offers a fully automatic generation of highly optimized target codes, customized for the specific execution platform at hand. Initially, SPIRAL was targeted at problem domains in digital signal processing, later also at basic linear algebra. We open SPIRAL up to a new, practically relevant and challenging domain: multigrid solvers. SPIRAL is driven by algebraic transformation rules. We specify a set of such rules for a simple multigrid solver with a Richardson smoother for a discretized square 2D Poisson equation with Dirichlet boundary conditions. We present the target code that SPIRAL generates in static single-assignment form and discuss its performance. While this example required no changes of or extensions to the SPIRAL system, more complex multigrid solvers may require small adaptations

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“…This formula features the indexed composition and the indexed tensor whose subscripts describe the number of the current iteration. More details are available in [34].…”

Section: Operator Language: Kernels and Algorithmsmentioning

confidence: 99%

“…In particular, note that we are not limited to these four codes. An online interface is provided to generate decoders on demand [34,46].…”

Section: Generating Programs For Ol Formulasmentioning

confidence: 99%

Operator Language: A Program Generation Framework for Fast Kernels

Franchetti

Mesmay

McFarlin

et al. 2009

Domain-Specific Languages

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Abstract. We present the Operator Language (OL), a framework to automatically generate fast numerical kernels. OL provides the structure to extend the program generation system Spiral beyond the transform domain. Using OL, we show how to automatically generate library functionality for the fast Fourier transform and multiple non-transform kernels, including matrix-matrix multiplication, synthetic aperture radar (SAR), circular convolution, sorting networks, and Viterbi decoding. The control flow of the kernels is data-independent, which allows us to cast their algorithms as operator expressions. Using rewriting systems, a structural architecture model and empirical search, we automatically generate very fast C implementations for state-of-the-art multicore CPUs that rival hand-tuned implementations.

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Computer Generation of Efficient Software Viterbi Decoders

Cited by 10 publications

References 17 publications

Efficient parallel implementation of three‐point viterbi decoding algorithm on CPU, GPU, and FPGA

Efficient parallel implementation of three‐point viterbi decoding algorithm on CPU, GPU, and FPGA

Algebraic description and automatic generation of multigrid methods in SPIRAL

Operator Language: A Program Generation Framework for Fast Kernels

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