A Novel Procedure for Implementing a Turbo Decoder on a GPU with Coalesced Memory Access

Abstract

The sub-blocking algorithm has been known as a core component in implementing a turbo decoder using a Graphic Processing Unit (GPU) to use as many cores in the GPU as possible for parallel processing. However, even though the sub-blocking algorithm allows a large number of threads in a given GPU to be adopted for processing a large number of sub-blocks in parallel, each thread must access the global memory with strided addresses, which results in uncoalesced memory access. Because uncoalesced memory access causes a lot of unnecessary memory transactions, the memory bandwidth efficiency drops significantly, possibly as low as 1/8 in the case of an Long Term Evolution (LTE) turbo decoder, depending upon the compute capability of a GPU. In this paper, we present a novel method for converting uncoalesced memory access into coalesced access in a way that completely recovers the memory bandwidth efficiency to 100 % without additional overhead. Our experimental tests, performed with NVIDIA's Geforce GTX 780 Ti GPU, show that the proposed method can enhance the throughput by nearly 30 % compared with a conventional turbo decoder that suffers from uncoalesced memory access. Throughput provided by the proposed method has been observed to be 51.4 Mbps when the number of iterations and that of sub-blocks are set to 6 and 32, respectively, in our experimental tests, which far exceeds the performance of previous works implemented the Max-Log-MAP algorithm.