Abstract. Many bioinformatics studies require the analysis of RNA or DNA structures. More specifically, extensive work is done to elaborate efficient algorithms able to predict the 2-D folding structures of RNA or DNA sequences. However, the high computational complexity of the algorithms, combined with the rapid increase of genomic data, triggers the need of faster methods. Current approaches focus on parallelizing these algorithms on multiprocessor systems or on clusters, yielding to good performance but at a relatively high cost. Here, we explore the use of computer graphics hardware to speed up these algorithms which, theoretically, provide both high performance and low cost. We use the CUDA programming language to harness the power of NVIDIA graphic cards for general computation with a C-like environment. Performances on recent graphic cards achieve a ×17 speed-up.

Abstract: Problem statement: Finding an accurate RNA structural alignment from primary sequence due to it is time consuming and computationally NP-hard problem is a major bioinformatics challenge. According to our investigation majority of current researches were concerned on achieving faster execution time, improving space complexity and better cache management. Recently one research introduced cache-efficient Chip Multiprocessor (CMP) algorithms with good speed-up to exploit parallelism in detection the critical path length. Our contribution in this article was a comprehensive survey of methods for solving RNA secondary structure prediction with Pseudoknots (PK) and sequence alignment in bioinformatics. The aim was to highlight the challenges related issues which would provide sufficient information to assist the new coming researchers in this field as well as a good reference guide for bioinformatics professionals. Approach: We computed various algorithms that predicted an RNA molecules secondary structure from primary sequence, without pseudoknots from one side and pseudoknotted RNA secondary structure in the other side. Furthermore, we also reviewed and compared in two tables the methods that developed for RNA structural predictions. Results: Our findings of this survey confirmed that Dynamic Programming (DP) method via CMP algorithms can be used to predict the RNA secondary structure with simple PK and it gives good results. Conclusion: The methods for predicting RNA's structural are coming in two groups: Firstly, pseudoknotted RNA structural problem is computationally complex and secondly, common methods significantly gave not accurate enough results for predicting pseudoknotted RNA.

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To make efficient use of CMPs with tens to hundreds of cores, it is often necessary toexploit fine-grain parallelism. However, managing tasks of a few thousand instructions is particularly challenging, as the runtime must ensure load balance without compromisinglocalityandintroducingsmalloverheads.Software-onlyschedulers can implement various scheduling algorithms that match the characteristics of different applications and programming models, but suffersignificant overheads astheysynchronize andcommunicatetaskinformationoverthedeepcachehierarchyofalarge-scale CMP. To reduce these costs, hardware-only schedulers like Carbon, which implement task queuing and scheduling in hardware, have been proposed. However, a hardware-only solution fixes the scheduling algorithm and leavesno room for other uses of the custom hardware. This paper presents a combined hardware-software approach to build fine-grain schedulers that retain the flexibility of software schedulers while being as fast andscalable as hardware ones. Wepropose asynchronous directmessages (ADM),asimplearchitectural extension that provides direct exchange of asynchronous, shortmessages betweenthreads intheCMPwithoutgoingthrough the memory hierarchy. ADMissufficient toimplement a familyof novel, software-mostly schedulers that rely on low-overhead messaging to efficiently coordinate scheduling and transfer task information. These schedulers matchand often exceed the performance and scalability of Carbon when using the same scheduling algorithm. When the ADM runtime tailors its scheduling algorithm to application characteristics,it outperforms Carbonby upto70%.