Implementations of relational operators on GPU processors have resulted in order of magnitude speedups compared to their multicore CPU counterparts. Here we focus on the efficient implementation of string matching operators common in SQL queries. Due to different architectural features the optimal algorithm for CPUs might be suboptimal for GPUs. GPUs achieve high memory bandwidth by running thousands of threads, so it is not feasible to keep the working set of all threads in the cache in a naive implementation. In GPUs the unit of execution is a group of threads and in the presence of loops and branches, threads in a group have to follow the same execution path; if some threads diverge, then different paths are serialized. We study the cache memory efficiency of single- and multi-pattern string matching algorithms for conventional and pivoted string layouts in the GPU memory. We evaluate the memory efficiency in terms of memory access pattern and achieved memory bandwidth for different parallelization methods. To reduce thread divergence, we split string matching into multiple steps. We evaluate the different matching algorithms in terms of average- and worst-case performance and compare them against state-of-the-art CPU and GPU libraries. Our experimental evaluation shows that thread and memory efficiency affect performance significantly and that our proposed methods outperform previous CPU and GPU algorithms in terms of raw performance and power efficiency. The Knuth---Morris---Pratt algorithm is a good choice for GPUs because its regular memory access pattern makes it amenable to several GPU optimizations.

GPU-accelerated string matching for database applications

This paper presents an alternative approach to the sequential data classification, based on traditional machine learning algorithms (neural networks, principal component analysis, multivariate Gaussian anomaly detector) and finding the shortest path in a directed acyclic graph, using A* algorithm with a regression-based heuristic. Palm gestures were used as an example of the sequential data and a quadrocopter was the controlled object. The study includes creation of a conceptual model and practical construction of a system using the GPU to ensure the realtime operation. The results present the classification accuracy of chosen gestures and comparison of the computation time between the CPUand GPU-based solutions.

/pdf/sequential-classification-of-palm-gestures-based-on-a-2er8m2hc6s.pdf

Sequential Classification of Palm Gestures Based on A* Algorithm and MLP Neural Network for Quadrocopter Control

This paper presents algorithms for the parallelization of inference in hidden Markov models (HMMs) In particular, we propose a parallel forward-backward type of filtering and smoothing algorithm as well as a parallel Viterbi-type maximum-a-posteriori (MAP) algorithm We define associative elements and operators to pose these inference problems as all-prefix-sums computations and parallelize them using the parallel-scan algorithm The advantage of the proposed algorithms is that they are computationally efficient in HMM inference problems with long time horizons We empirically compare the performance of the proposed methods to classical methods on a highly parallel graphics processing unit (GPU)

pdf/temporal-parallelization-of-inference-in-hidden-markov-1m9dkqunpd.pdf

Temporal Parallelization of Inference in Hidden Markov Models

Cognitive radio is a growing field in communications which allows a radio to automatically configure its transmission or reception properties in order to reduce interference, provide better quality of service, or allow for more users in a given spectrum. Such processes require several complex features that are currently being utilized in cognitive radio. Two such features, spectrum sensing and identification, have been implemented in numerous ways, however, they generally suffer from high computational complexity. Additionally, Hidden Markov Models (HMMs) are a widely used mathematical modeling tool used in various fields of engineering and sciences. In electrical and computer engineering, it is used in several areas, including speech recognition, handwriting recognition, artificial intelligence, queuing theory, and are used to model fading in communication channels. The research presented in this thesis proposes a new approach to spectrum identification using a parallel implementation of Hidden Markov Models. Algorithms involving HMMs are usually implemented in the traditional serial manner, which have prohibitively long runtimes. In this work, we study their use in parallel implementations and compare our approach to traditional serial implementations. Timing and power measurements are taken and used to show that the parallel implementation can achieve well over 100× speedup in certain situations. To demonstrate the utility of this new parallel algorithm using graphics processing units (GPUs), a new method for signal identification is proposed for both serial and parallel implementations using HMMs. The method achieved high recognition at -10 dB Eb/N0. HMMs can benefit from parallel implementation in certain circumstances, specifically, in models that have many states or when multiple models are used in conjunction.

Massively Parallel Hidden Markov Models for Wireless Applications

Viterbi algorithm is used in different scientific applications including biological sequence alignment, speech recognition, and probabilistic inference However, high computational complexity of the Viterbi algorithm is a major concern Accelerating the Viterbi algorithm is important, especially when the number of states or the length of the sequences increase significantly In this paper, a parallel solution to improve the performance of Viterbi algorithm is presented This is achieved by formulating a matrix product based algorithm This algorithm has been mapped to a NVIDIA graphics processing unit The performance for different parameters and realizations are compared The results depicts matrix product is not a viable option for small number of states However, matrix product solution using shared memory for large number of states gains good performance when compared with the serial version

Accelerating Viterbi algorithm on graphics processing units

It is compute-intensive to evaluate the probability of an observation sequence on a hidden Markov model. Some fast algorithms exit, the forward-backward procedure is the most popular one among them. The forward-backward procedure can save much computation, but its time complexity is N2T, in other words, there is a high computational complexity in the algorithm. In this paper, we present a parallel evaluation algorithm using a commodity graphics processing unit. The algorithm exploits the single-instruction-multiple-thread architecture of GPU to get high-performance. First, the forward probabilities are calculated in parallel, and then they are summed up also in parallel to get the probability of an observation sequence. The optimal using of memory bandwidth is studied in the algorithm to obtain the best performance. The algorithm was implemented on a NVIDIA 9800 GTX+ GPU, experimental results showed the parallel algorithm can evaluate the probability of an observation sequence on a hidden Markov model 4∼25 times fast than the classic one does.

The fast evaluation of hidden Markov models on GPU

Profile Hidden Markov Models are used as a popular tool in bioinformatics research and a regular task is to compare a set of protein sequences with a database of models according to sequences' score on these models. However, it suffers from long runtimes on PC platforms, and the runtimes are increasing rapidly due to the rapid growth in size of both sequences and models. In this paper, we present a Viterbi algorithm running on graphic processing units to score sequences, a method padding HMMs and a memory hierarchy are also introduced, these strategies can promote running efficiency in parallel and reduce impact of the bottleneck from buses. Experimental results show the runtimes are saved by the method dramatically.

The fast Viterbi algorithm caching Profile Hidden Markov Models on graphic processing units

A parallel algorithm on graphic processing units is presented to evaluate an observation sequence on hidden Markov models quickly. The evaluation is compute-intensive when there are thousands of or more Markov models and there are many states in each model. The compute-load can be reduced by the proposed algorithm greatly, but the hardware needed by the algorithm is not so expensive as the other accelerating hardware and is used widely in PCs. In a supervised recognition task, all of the hidden Markov models must be sorted by their evaluation probability in descent order, one can select a proper model from the list quickly. A sorting network was implemented in the algorithm to run on graphic processing units to sort the models in parallel by their evaluation probability. The algorithm was tested on a NVIDIA 9800 GTX+ graphic processing units, experimental results showed the parallel algorithm can evaluate the probability of an observation sequence on hidden Markov models 10∼100 times fast than the serial algorithm does on Pentium E5200 CPU.

The parallel evaluation of hidden Markov models on graphic processing units in supervised recognition

A profile Hidden Markov Model (HMM) is well suited for representing profiles of multiple sequences alignments, and it has been becoming the main method of multiple sequences alignments in bioinformatics. The scoring of sequences on profile HMMs is compute-intensive, especially when there are many Markov models and many states in each model. A parallel algorithm for Graphic Processing Unit (GPU)s is presented to score multiple sequences quickly on profile HMMs, and it featured with delete states elimination to reduce the compute-load greatly using a commodity graphics processing unit. The access to the parameters of profile HMMs is accelerated by allocating space in proper memory hierarchy. The algorithm was tested on a NVIDIA 9800 GTX+ graphic processing unit, experimental results showed the parallel algorithm can score multiple sequences on profile HMMs 8∼50 times faster than the serial algorithm does on Pentium E5200 CPU.

The scoring sequences on profile Hidden Markov Models with delete states elimination by GPUs

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Papers

The fast evaluation of hidden Markov models on GPU

The fast Viterbi algorithm caching Profile Hidden Markov Models on graphic processing units

The parallel evaluation of hidden Markov models on graphic processing units in supervised recognition

The scoring sequences on profile Hidden Markov Models with delete states elimination by GPUs