ASCII Art Generation Using the Local Exhaustive Search on the GPU

2014 Second International Symposium on Computing and Networking

2014

Self Cite

The Hierarchical Memory Machine (HMM) is a theoretical parallel computing model that captures the essence of architecture of CUDA-enabled GPUs. The main contribution of this paper is to present an efficient implementation of the Ç´Ò ¿ µ-time dynamic programming algorithm for solving the optimal triangulation problem for a convex Ò-gon in the HMM. Although the HMM can run a lot of threads in parallel, it is very hard to accelerate computation involving complicated memory access such as the dynamic programming for the optimal triangulation problem. It is often the case that the acceleration rate is limited to the bandwidth Û of the global memory for problems involving complicated stride memory access. Quite surprisingly, our implementation of the dynamic programing algorithm for solving the optimal triangulation problem runs Ç´Ò ¿ Û ¾ µ time units using Ñ Ü´ÛÄ Û ¾ Ðµ threads on the HMM with bandwidth Û, global memory latency Ä and shared memory latency Ð. Hence, this parallel algorithm achieves the acceleration rate of more than Û although the dynamic programming algorithm involves complicated stride memory access. Also, we prove that this parallel algorithm is time optimal when Ä Ç´ÛÐµ.

Section: Casementioning

confidence: 99%

“…The GPU (Graphics Processing Unit) is a specialized circuit designed to accelerate computation for building and manipulating images [1], [2], [3]. Latest GPUs are designed for general purpose computing and can perform computation in applications traditionally handled by the CPU.…”

Section: Introductionmentioning

confidence: 99%

A Time Optimal Parallel Algorithm for the Dynamic Programming on the Hierarchical Memory Machine

2014 Second International Symposium on Computing and Networking

2014

Self Cite

“…Using CUDA, we can develop parallel processing programs to be implemented in GPUs. Therefore, many studies have been devoted to implement parallel algorithms using CUDA [3,8,10,16,18,19]. The ideas of our GPU implementation are…”

Section: Introductionmentioning

confidence: 99%

GPU-accelerated Exhaustive Verification of the Collatz Conjecture

Honda

Ito

2017

IJNC

Self Cite

The main contribution of this paper is to present an implementation that performs the exhaustive search to verify the Collatz conjecture using a GPU. Consider the following operation on an arbitrary positive number: if the number is even, divide it by two, and if the number is odd, triple it and add one. The Collatz conjecture asserts that, starting from any positive number m, repeated iteration of the operations eventually produces the value 1. We have implemented it on NVIDIA GeForce GTX TITAN X and evaluated the performance. The experimental results show that, our GPU implementation can verify 1.31 × 10 12 64-bit numbers per second. While the sequential CPU implementation on Intel Core i7-4790 can verify 5.25×10 9 64-bit numbers per second. Thus, our implementation on the GPU attains a speed-up factor of 249 over the sequential CPU implementation. Additionally, we accelerated the computation of counting the number of the above operations until a number reaches 1, called delay that is one of the mathematical interests for the Collatz conjecture by the GPU. Using a similar idea, we achieved a speed-up factor of 73.

“…A GPU (Graphics Processing Unit) is a specialized circuit designed to accelerate computation for building and manipulating images [1]- [3]. Latest GPUs are designed for general purpose computing and can perform computation in applications traditionally handled by the CPU.…”

Section: Introductionmentioning

confidence: 99%

Fully Parallelized LZW Decompression for CUDA-Enabled GPUs

Funasaka

IEICE Trans. Inf. & Syst.

Ito

2016

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SUMMARYThe main contribution of this paper is to present a workoptimal parallel algorithm for LZW decompression and to implement it in a CUDA-enabled GPU. Since sequential LZW decompression creates a dictionary table by reading codes in a compressed file one by one, it is not easy to parallelize it. We first present a work-optimal parallel LZW decompression algorithm on the CREW-PRAM (Concurrent-Read Exclusive-Write Parallel Random Access Machine), which is a standard theoretical parallel computing model with a shared memory. We then go on to present an efficient implementation of this parallel algorithm on a GPU. The experimental results show that our GPU implementation performs LZW decompression in 1.15 milliseconds for a gray scale TIFF image with 4096 × 3072 pixels stored in the global memory of GeForce GTX 980. On the other hand, sequential LZW decompression for the same image stored in the main memory of Intel Core i7 CPU takes 50.1 milliseconds. Thus, our parallel LZW decompression on the global memory of the GPU is 43.6 times faster than a sequential LZW decompression on the main memory of the CPU for this image. To show the applicability of our GPU implementation for LZW decompression, we evaluated the SSD-GPU data loading time for three scenarios. The experimental results show that the scenario using our LZW decompression on the GPU is faster than the others.