Multi-core platforms for signal processing: source and channel coding

Sousa, Leonel; Momcilovic, Svetislav; Silva, Vítor; Falcão, Gabriel

doi:10.1109/icme.2009.5202874

Cited by 3 publications

(2 citation statements)

References 7 publications

(10 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This enables every bit in a frame to be processed concurrently, hence achieving a much higher degree of parallelism than the previously demonstrated in the literature. Thus, the FPTD is well suited for multi-core processors [23], potentially facilitating a significant processing throughput gain, relative to the stateof-the-art. However, our previous work of [22] considered the FPTD at a purely algorithmic level, without addressing its hardware implementation.…”

Section: Introductionmentioning

confidence: 99%

Implementation of a Fully-Parallel Turbo Decoder on a General-Purpose Graphics Processing Unit

et al. 2016

View full text Add to dashboard Cite

Turbo codes comprising a parallel concatenation of upper and lower convolutional codes are widely employed in the state-of-the-art wireless communication standards, since they facilitate transmission throughputs that closely approach the channel capacity. However, this necessitates high processing throughputs in order for the turbo code to support real-time communications. In the state-of-the-art turbo code implementations, the processing throughput is typically limited by the data dependences that occur within the forward and backward recursions of the Log-BCJR algorithm, which is employed during turbo decoding. In contrast to the highly serial Log-BCJR turbo decoder, we have recently proposed a novel fully parallel turbo decoder (FPTD) algorithm, which can eliminate the data dependences and perform fully parallel processing. In this paper, we propose an optimized FPTD algorithm, which reformulates the operation of the FPTD algorithm so that the upper and lower decoders have identical operation, in order to support single instruction multiple data operation. This allows us to develop a novel general purpose graphics processing unit (GPGPU) implementation of the FPTD, which has application in software-defined radios and virtualized cloud-radio access networks. As a benefit of its higher degree of parallelism, we show that our FPTD improves the higher processing throughput of the Log-BCJR turbo decoder by between 2.3 and 9.2 times, when employing a high-specification GPGPU. However, this is achieved at the cost of a moderate increase of the overall complexity by between 1.7 and 3.3 times.INDEX TERMS Fully-parallel turbo decoder, parallel processing, GPGPU computing, software defined radio, could radio access network.

show abstract

Section: Introductionmentioning

confidence: 99%

Implementation of a Fully-Parallel Turbo Decoder on a General-Purpose Graphics Processing Unit

et al. 2016

View full text Add to dashboard Cite

show abstract

“…The physical layer baseband processing generally requires very high bandwidth and thus high processing power. Thus, multi-core processors are often employed in modern, embedded communication devices [7,8]. Also, GPU is often adopted to achieve high computational power [9].…”

Section: Introductionmentioning

confidence: 99%

Parallel LDPC decoding using CUDA and OpenMP

Park

Chung

2011

J Wireless Com Network

View full text Add to dashboard Cite

Digital mobile communication technologies, such as next generation mobile communication and mobile TV, are rapidly advancing. Hardware designs to provide baseband processing of new protocol standards are being actively attempted, because of concurrently emerging multiple standards and diverse needs on device functions, hardwareonly implementation may have reached a limit. To overcome this challenge, digital communication system designs are adopting software solutions that use central processing units or graphics processing units (GPUs) to implement communication protocols. In this article we propose a parallel software implementation of low density parity check decoding algorithms, and we use a multi-core processor and a GPU to achieve both flexibility and high performance. Specifically, we use OpenMP for parallelizing software on a multi-core processor and Compute Unified Device Architecture (CUDA) for parallel software running on a GPU. We process information on H-matrices using OpenMP pragmas on a multi-core processor and execute decoding algorithms in parallel using CUDA on a GPU. We evaluated the performance of the proposed implementation with respect to two different code rates for the China Multimedia Mobile Broadcasting (CMMB) standard, and we verified that the proposed implementation satisfies the CMMB bandwidth requirement.

show abstract

LDPC decoding for CMMB utilizing OpenMP and CUDA parallelization

Park

Chung

2011

The 17th Asia Pacific Conference on Communications

View full text Add to dashboard Cite

Multi-core platforms for signal processing: source and channel coding

Cited by 3 publications

References 7 publications

Implementation of a Fully-Parallel Turbo Decoder on a General-Purpose Graphics Processing Unit

Implementation of a Fully-Parallel Turbo Decoder on a General-Purpose Graphics Processing Unit

Parallel LDPC decoding using CUDA and OpenMP

LDPC decoding for CMMB utilizing OpenMP and CUDA parallelization

Contact Info

Product

Resources

About