254 research outputs found
A study on the effect of stroop test on the formation of students discipline by using the Heart Rate Variability (HRV) technique
Discipline refers to self-control and individual behaviour. Other than that, discipline is an important element in the formation of integrity level. The objective of the study is to assess the effects of using the Stroop test of biofeedback protocol in order to evaluate individual level of discipline. A clinical study has been conducted on 50 participants which is the participants is a undergraduate student from Universiti Malaysia Pahang, who were divided into two groups. First group is students get high achiever and second group is students get low achierver in academic. The Heart Rate Variability (HRV) technique has been used in the assessment of this protocol. The findings show that there was a positive relationship between the Stroop test and the students discipline that those who excelled managed to get higher score of LF spectrum as compared to HF and VLF, while the students with lower achievement showed higher score of VLF and HF spectrum than LF. In conclusion, this test is one of the tests that can be used in increasing the level of individual discipline
Revisiting the Speed-versus-Sensitivity Tradeoff in Pairwise Sequence Search
The Smith-Waterman algorithm is a dynamic programming method for determining optimal local alignments between nucleotide or protein sequences. However, it suffers from quadratic time and space complexity. As a result, many algorithmic and architectural enhancements have been proposed to solve this problem, but at the cost of reduced sensitivity in the algorithms or signiďŹcant expense in hardware, respectively. Hence, there exists a need to evaluate the tradeoffs between the different solutions. This motivation, coupled with the lack of an evaluation metric to quantify these tradeoffs leads us to formally deďŹne and quantify the sensitivity of homology search methods so that tradeoffs between sequence-search solutions can be evaluated in a quantitative manner. As an example, though the BLAST algorithm executes signiďŹcantly faster than Smith-Waterman, we ďŹnd that BLAST misses 80% of the signiďŹcant sequence alignments. This paper then presents a highly efďŹcient parallelization of the Smith-Waterman algorithm on the Cell Broadband Engine, a novel hybrid multicore architecture that drives the PlayStation 3 (PS3) game consoles, and emulates BLAST by repeatedly executing the parallelized Smith-Waterman algorithm to search for a query in a given sequence database. Through an innovative mapping of the optimal Smith-Waterman algorithm onto a cluster of PlayStation 3 nodes, our implementation delivers a 10-fold speed-up over a high-end multicore architecture and an 88-fold speed-up over a non-accelerated PS3. Finally, we compare the performance of our implementation of the Smith-Waterman algorithm to that of BLAST and the canonical Smith-Waterman implementation, based on a combination of three factors â execution time (speed), sensitivity, and the actual cost of de-ploying each solution. In the end, our parallelized Smith-Waterman algorithm approaches the speed of BLAST while maintaining ideal sensitivity and achieving low cost through the use of PlayStation 3 game consoles
Design and analysis of an accelerated seed generation stage for BLASTP on the Mercury system - Master\u27s Thesis, August 2006
NCBI BLASTP is a popular sequence analysis tool used to study the evolutionary relationship between two protein sequences. Protein databases continue to grow exponentially as entire genomes of organisms are sequenced, making sequence analysis a computationally demanding task. For example, a search of the E. coli. k12 proteome against the GenBank Non-Redundant database takes 36 hours on a standard workstation. In this thesis, we look to address the problem by accelerating protein searching using Field Programmable Gate Arrays. We focus our attention on the BLASTP heuristic, building on work done earlier to accelerate DNA searching on the Mercury platform. We analyze the performance characteristics of the BLASTP algorithm and explore the design space of the seed generation stage in detail. We propose a hardware/software architecture and evaluate the performance of the individual stage, and its effect on the overall BLASTP pipeline running on the Mercury system. The seed generation stage is 13x faster than the software equivalent, and the integrated BLASTP pipeline is predicted to yield a speedup of 50x over NCBI BLASTP. Mercury BLASTP also shows a 2.5x speed improvement over the only other BLASTP-like accelerator for FPGAs while consuming far fewer logic resources
Comparative Analysis of Computationally Accelerated NGS Alignment
The Smith-Waterman algorithm is the basis of most current sequence alignment technology, which can be used to identify similarities between sequences for cancer detection and treatment because it provides researchers with potential targets for early diagnosis and personalized treatment. The growing number of DNA and RNA sequences available to analyze necessitates faster alignment processes than are possible with current iterations of the Smith-Waterman (S-W) algorithm. This project aimed to identify the most effective and efficient methods for accelerating the S-W algorithm by investigating recent advances in sequence alignment. Out of a total of 22 articles considered in this project, 17 articles had to be excluded from the study due to lack of standardization of data reporting. Only one study by Chen et al. obtained in this project contained enough information to compare accuracy and alignment speed. When accuracy was excluded from the criteria, five studies contained enough information to rank their efficiency. The study conducted by Rucci et al. was the fastest at 268.83 Giga Cell Updates Per Second (GCUPS), and the method by PĂŠrez-Serrano et al. came close at 229.93 GCUPS while testing larger sequences. It was determined that reporting standards in this field are not sufficient, and the study by Chen et al. should set a benchmark for future reporting
Accelerated Profile HMM Searches
Profile hidden Markov models (profile HMMs) and probabilistic inference methods have made important contributions to the theory of sequence database homology search. However, practical use of profile HMM methods has been hindered by the computational expense of existing software implementations. Here I describe an acceleration heuristic for profile HMMs, the âmultiple segment Viterbiâ (MSV) algorithm. The MSV algorithm computes an optimal sum of multiple ungapped local alignment segments using a striped vector-parallel approach previously described for fast Smith/Waterman alignment. MSV scores follow the same statistical distribution as gapped optimal local alignment scores, allowing rapid evaluation of significance of an MSV score and thus facilitating its use as a heuristic filter. I also describe a 20-fold acceleration of the standard profile HMM Forward/Backward algorithms using a method I call âsparse rescalingâ. These methods are assembled in a pipeline in which high-scoring MSV hits are passed on for reanalysis with the full HMM Forward/Backward algorithm. This accelerated pipeline is implemented in the freely available HMMER3 software package. Performance benchmarks show that the use of the heuristic MSV filter sacrifices negligible sensitivity compared to unaccelerated profile HMM searches. HMMER3 is substantially more sensitive and 100- to 1000-fold faster than HMMER2. HMMER3 is now about as fast as BLAST for protein searches
High performance reconfigurable architectures for biological sequence alignment
Bioinformatics and computational biology (BCB) is a rapidly developing
multidisciplinary field which encompasses a wide range of domains, including genomic
sequence alignments. It is a fundamental tool in molecular biology in searching for
homology between sequences. Sequence alignments are currently gaining close attention due
to their great impact on the quality aspects of life such as facilitating early disease diagnosis,
identifying the characteristics of a newly discovered sequence, and drug engineering. With
the vast growth of genomic data, searching for a sequence homology over huge databases
(often measured in gigabytes) is unable to produce results within a realistic time, hence the
need for acceleration. Since the exponential increase of biological databases as a result of the
human genome project (HGP), supercomputers and other parallel architectures such as the
special purpose Very Large Scale Integration (VLSI) chip, Graphic Processing Unit (GPUs)
and Field Programmable Gate Arrays (FPGAs) have become popular acceleration platforms.
Nevertheless, there are always trade-off between area, speed, power, cost, development time
and reusability when selecting an acceleration platform. FPGAs generally offer more
flexibility, higher performance and lower overheads. However, they suffer from a relatively
low level programming model as compared with off-the-shelf microprocessors such as
standard microprocessors and GPUs. Due to the aforementioned limitations, the need has
arisen for optimized FPGA core implementations which are crucial for this technology to
become viable in high performance computing (HPC).
This research proposes the use of state-of-the-art reprogrammable system-on-chip
technology on FPGAs to accelerate three widely-used sequence alignment algorithms; the
Smith-Waterman with affine gap penalty algorithm, the profile hidden Markov model
(HMM) algorithm and the Basic Local Alignment Search Tool (BLAST) algorithm. The
three novel aspects of this research are firstly that the algorithms are designed and
implemented in hardware, with each core achieving the highest performance compared to the
state-of-the-art. Secondly, an efficient scheduling strategy based on the double buffering
technique is adopted into the hardware architectures. Here, when the alignment matrix
computation task is overlapped with the PE configuration in a folded systolic array, the
overall throughput of the core is significantly increased. This is due to the bound PE
configuration time and the parallel PE configuration approach irrespective of the number of
PEs in a systolic array. In addition, the use of only two configuration elements in the PE optimizes hardware resources and enables the scalability of PE systolic arrays without
relying on restricted onboard memory resources. Finally, a new performance metric is
devised, which facilitates the effective comparison of design performance between different
FPGA devices and families. The normalized performance indicator (speed-up per area per
process technology) takes out advantages of the area and lithography technology of any
FPGA resulting in fairer comparisons.
The cores have been designed using Verilog HDL and prototyped on the Alpha Data
ADM-XRC-5LX card with the Virtex-5 XC5VLX110-3FF1153 FPGA. The implementation
results show that the proposed architectures achieved giga cell updates per second (GCUPS)
performances of 26.8, 29.5 and 24.2 respectively for the acceleration of the Smith-Waterman
with affine gap penalty algorithm, the profile HMM algorithm and the BLAST algorithm. In
terms of speed-up improvements, comparisons were made on performance of the designed
cores against their corresponding software and the reported FPGA implementations. In the
case of comparison with equivalent software execution, acceleration of the optimal
alignment algorithm in hardware yielded an average speed-up of 269x as compared to the
SSEARCH 35 software. For the profile HMM-based sequence alignment, the designed core
achieved speed-up of 103x and 8.3x against the HMMER 2.0 and the latest version of
HMMER (version 3.0) respectively. On the other hand, the implementation of the gapped
BLAST with the two-hit method in hardware achieved a greater than tenfold speed-up
compared to the latest NCBI BLAST software. In terms of comparison against other reported
FPGA implementations, the proposed normalized performance indicator was used to
evaluate the designed architectures fairly. The results showed that the first architecture
achieved more than 50 percent improvement, while acceleration of the profile HMM
sequence alignment in hardware gained a normalized speed-up of 1.34. In the case of the
gapped BLAST with the two-hit method, the designed core achieved 11x speed-up after
taking out advantages of the Virtex-5 FPGA. In addition, further analysis was conducted in
terms of cost and power performances; it was noted that, the core achieved 0.46 MCUPS per
dollar spent and 958.1 MCUPS per watt. This shows that FPGAs can be an attractive
platform for high performance computation with advantages of smaller area footprint as well
as represent economic âgreenâ solution compared to the other acceleration platforms. Higher
throughput can be achieved by redeploying the cores on newer, bigger and faster FPGAs
with minimal design effort
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