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Accelerating Extended Hamming Code Decoders on Graphic Processing Units for High Speed Communication
ISLAM, Md Shohidul,KIM, Jong-Myon 'Institute of Electronics, Information and Communi 2014 IEICE TRANSACTIONS ON COMMUNICATIONS - Vol.eb97 No.5
<P>Emerging networks characterized by growing speed and data insensitivity demand faster and scalable error handling. Prevalent decoders are based on dedicated hardware, offering considerable processing speed, but limited flexibility, programmability and scalability. This paper proposes an efficient approach to accelerate the extended-Hamming code decoder using a graphics processing unit (GPU), chosen for its low cost and extremely high-throughput parallel-computing capability. This paper compares the performance of the GPU-based approach with the equivalent sequential approaches that are performed on a central processing unit (CPU) and Texas Instruments TMS320C6742 digital signal processor (DSP) with varying packet sizes and error tolerances. Experimental results demonstrate that the proposed GPU-based approach outperforms the sequential approaches in terms of execution time and energy consumption.</P>
Spectral Modeling Synthesis of Haegeum using GPU
Md Shohidul Islam,Md Rashedul Islam,Fahmid Al Farid,Jong-Myon Kim(김종면) 한국컴퓨터정보학회 2014 한국컴퓨터정보학회 학술발표논문집 Vol.22 No.1
This paper presents a parallel approach of formant synthesis method for haegeum on graphics processing units (GPU) using spectral modeling. Spectral modeling synthesis (SMS) is a technique that models time-varying spectra as a combination of sinusoids and a time-varying filtered noise component. A second-order digital resonator by the impulse-invariant transform (IIT) is applied to generate deterministic components and the results are band-pass filtered to adjust magnitude. The noise is calculated by first generating the sinusoids with formant synthesis, subtracting them from the original sound, and then removing some harmonics remained. The synthesized sounds are consequently by adding sinusoids, which are shown to be similar to the original Haegeum sounds. Furthermore, GPU accelerates the synthesis process enabling- real time music synthesis system development, supporting more sound effect, and multiple musical sound compositions.
Computationally Efficient Implementation of a Hamming Code Decoder Using Graphics Processing Unit
Md Shohidul Islam,김철홍,김종면 한국통신학회 2015 Journal of communications and networks Vol.17 No.2
This paper presents a computationally efficient implementation of a Hamming code decoder on a graphics processing unit (GPU) to support real-time software-defined radio, which is a software alternative for realizing wireless communication. The Hamming code algorithm is challenging to parallelize effectively on a GPU because it works on sparsely located data items with several conditional statements, leading to non-coalesced, long latency, global memory access, and huge thread divergence. To address these issues, we propose an optimized implementation of the Hamming code on the GPU to exploit the higher parallelism inherent in the algorithm. Experimental results using a compute unified device architecture (CUDA)-enabled NVIDIA GeForce GTX 560, including 335 cores, revealed that the proposed approach achieved a 99x speedup versus the equivalent CPU-based implementation.