Jung-Ho Um

A cross-correlation (XCorr) score function is one of the most popular score functions utilized to search peptide identifications in databases, and many computer programs, such as SEQUEST, Comet, and Tide, currently use this score function. Recently, the HiXCorr algorithm was developed to speed up this score function for high-resolution spectra by improving the preprocessing step of the tandem mass spectra. However, despite the development of the HiXCorr algorithm, the score function is still slow because candidate peptides increase when post-translational modifications (PTMs) are considered in the search. We used a graphics processing unit (GPU) to develop the accelerating score function derived by combining Tide’s XCorr score function and the HiXCorr algorithm. Our method is 2.7 and 5.8 times faster than the original Tide and Tide-Hi, respectively, for 50 Da precursor tolerance. Our GPU-based method produced identical scores as did the CPU-based Tide and Tide-Hi. We propose the accelerating score function to search modifications using a single GPU. The software is available at https://github.com/Tide-for-PTM-search/Tide-for-PTM-search .

Layout-Aware Data Scheduler (LADS) data transfer tool, identifies and addresses the issues that lead to congestion on the path of an end-to-end data transfer in the terabit network environments. It exploits the underlying storage layout at each endpoint to maximize throughput without negatively impacting the performance of shared storage resources for other users. LADS can avoid congested storage elements within the shared storage resource, improving input/output bandwidth, and hence the data transfer rates across the high speed networks. However, absence of FT (fault tolerance) support in LADS results in data re-transmission overhead along with the possible integrity issues upon errors. In this paper, we propose object based logging methods to avoid transmitting the objects which are successfully written to Parallel File System (PFS) at the sink end. Depending on the number of logger files created, for the whole dataset, we classified our fault tolerance mechanisms into three different categories: File logger, Transaction logger and Universal logger. Also, to address space overhead of these object based logging mechanisms, we have proposed different methods of populating logger files with the information of the completed objects. We have evaluated the data transfer performance and recovery time overhead of the proposed object based logging fault tolerant mechanisms on LADS data transfer tool. Our experimental results show that, LADS in conjunction with proposed object based fault tolerance mechanisms exhibit an overhead of less than 1% with respect to data transfer time and total recovery time overhead is around 10% of total data transfer time at any fault point.