MCtandem: an efficient tool for large-scale peptide identification on many integrated core (MIC) architecture

Abstract

Background Tandem mass spectrometry (MS/MS)-based database searching is a widely acknowledged and widely used method for peptide identification in shotgun proteomics. However, due to the rapid growth of spectra data produced by advanced mass spectrometry and the greatly increased number of modified and digested peptides identified in recent years, the current methods for peptide database searching cannot rapidly and thoroughly process large MS/MS spectra datasets. A breakthrough in efficient database search algorithms is crucial for peptide identification in computational proteomics. Results This paper presents MCtandem, an efficient tool for large-scale peptide identification on Intel Many Integrated Core (MIC) architecture. To support big data processing capability, a novel parallel match scoring algorithm, named MIC-SDP (spectrum dot product), and its two-level parallelization are presented in MCtandem’s design. In addition, a series of optimization strategies on both the host CPU side and the MIC side, which includes pre-fetching, optimized communication overlapping scheme, multithreading and hyper-threading, are exploited to improve the execution performance. Conclusions For fair comparisons, we first set up experiments and verified the 28 fold times speedup on a single MIC against the original CPU-based implementation. We then execute the MCtandem for a very large dataset on an MIC cluster (a component of the Tianhe-2 supercomputer) and achieved much higher scalability than in a benchmark MapReduce-based programs, MR-Tandem. MCtandem is an open-source software tool implemented in C++. The source code and the parameter settings are available at https://github.com/LogicZY/MCtandem .

Methods for Proteogenomics Data Analysis, Challenges, and Scalability Bottlenecks: A Survey

“…These studies include Parallel Tandem [ 196 ] which spawns multiple instances of the original X!Tandem on distributed machines; X! !Tandem [ 207 ] achieves parallelism using owner-compute MPI processes; MR-Tandem [ 208 ] uses Map-Reduce instead of MPI for better speedup efficiency; MCtandem [ 209 ] employs Intel Many Integrated Core (MIC) architecture co-processor to speedup spectral dot products (SDP) for X!Tandem, and SW-Tandem [ 197 ] employs the Haswell AVX2 engine to speedup SDP computations on Sunway Taihulight supercomputer. SW-Tandem also spawns a manager process that distributes the experimental data to worker processes using a global queue for better load balancing.…”

Locality-sensitive hashing enables signal classification in high-throughput mass spectrometry raw data at scale

“…In the particular context of mass spectrometry, LSH has been used for looking up peptide sequences in databases [18] [19], to cluster different spectra for MS1 spectra on LC-MS data [20], and for fast database lookup on MS2 spectra [21].…”

HiCOPS: High Performance Computing Framework for Tera-Scale Database Search of Mass Spectrometry based Omics Data

“…As demonstrated by other big data fields [23], such limitations can be reduced by developing parallel algorithms that combine the computational power of thousands of processing elements across distributed-memory clusters, and supercomputers. We, and others have developed high-performance computing (HPC) techniques for processing of MS data including for multicore [3], [2], [10], [9], and distributed-memory architectures [24], [25] [26], [27], [28], [29]. Similar to serial algorithms, the objective of these HPC methods has been to speed up the arithmetic scoring part of the search engines, by spawning multiple (managed) instances of the original code, replicating the theoretical database, and splitting the experimental data.…”

“…!Tandem however by breaking computations into small Map and Reduce tasks (Map-Reduce model) exhibiting better parallel efficiency than the Parallel Tandem and X!!Tandem. MCtandem [27] and SW-Tandem [28] implement the same parallel design but offload the X!Tandem's expensive Spectral Dot Product (SDP) computations over Intel Many Integrated Core (MIC) co-processor and Haswell AVX2 vector instructions respectively. Both algorithms also implement optimization techniques including double buffering, pre-fetching, overlapped communication and computations and a task-distributor for better performance.…”