Search results for "Xeon"
showing 10 items of 20 documents
Pairwise DNA Sequence Alignment Optimization
2015
This chapter presents a parallel implementation of the Smith-Waterman algorithm to accelerate the pairwise alignment of DNA sequences. This algorithm is especially computationally demanding for long DNA sequences. Parallelization approaches are examined in order to deeply explore the inherent parallelism within Intel Xeon Phi coprocessors. This chapter looks at exploiting instruction-level parallelism within 512-bit single instruction multiple data instructions (vectorization) as well as thread-level parallelism over the many cores (multithreading using OpenMP). Between coprocessors, device-level parallelism through the compute power of clusters including Intel Xeon Phi coprocessors using M…
Bit-Parallel Approximate Pattern Matching on the Xeon Phi Coprocessor
2014
Bit-parallel pattern matching encodes calculated values in bit arrays. This approach gains its efficiency by performing multiple updates within a machine word. An important parameter is therefore the machine word size (e.g. 32 or 64 bits). With the increasing length of vector registers, the efficient mapping of bit-parallel pattern matching algorithms onto modern high performance computing architectures is becoming increasingly important. In this paper, we investigate an efficient implementation of the Wu-Manber approximate pattern matching algorithm on the Intel Xeon Phi coprocessor. This architecture features a 512-bit long vector processing unit (VPU) as well as a large number of process…
SWAPHI-LS: Smith-Waterman Algorithm on Xeon Phi coprocessors for Long DNA Sequences
2014
As an optimal method for sequence alignment, the Smith-Waterman (SW) algorithm is widely used. Unfortunately, this algorithm is computationally demanding, especially for long sequences. This has motivated the investigation of its acceleration on a variety of high-performance computing platforms. However, most work in the literature is only suitable for short sequences. In this paper, we present SWAPHI-LS, the first parallel SW algorithm exploiting emerging Xeon Phi coprocessors to accelerate the alignment of long DNA sequences. In SWAPHI-LS, we have investigated three parallelization approaches (naive, tiled, and distributed) in order to deeply explore the inherent parallelism within Xeon P…
XLCS: A New Bit-Parallel Longest Common Subsequence Algorithm on Xeon Phi Clusters
2019
Finding the longest common subsequence (LCS) of two strings is a classical problem in bioinformatics. A basic approach to solve this problem is based on dynamic programming. As the biological sequence databases are growing continuously, bit-parallel sequence comparison algorithms are becoming increasingly important. In this paper, we present XLCS, a new parallel implementation to accelerate the LCS algorithm on Xeon Phi clusters by performing bit-wise operations. We have designed an asynchronous IO framework to improve the data transfer efficiency. To make full use of the computing resources of Xeon Phi clusters, we use three levels of parallelism: node-level, thread-level and vector-level.…
SWAPHI: Smith-Waterman Protein Database Search on Xeon Phi Coprocessors
2014
The maximal sensitivity of the Smith-Waterman (SW) algorithm has enabled its wide use in biological sequence database search. Unfortunately, the high sensitivity comes at the expense of quadratic time complexity, which makes the algorithm computationally demanding for big databases. In this paper, we present SWAPHI, the first parallelized algorithm employing Xeon Phi coprocessors to accelerate SW protein database search. SWAPHI is designed based on the scale-and-vectorize approach, i.e. it boosts alignment speed by effectively utilizing both the coarse-grained parallelism from the many co-processing cores (scale) and the fine-grained parallelism from the 512-bit wide single instruction, mul…
Accelerating large-scale biological database search on Xeon Phi-based neo-heterogeneous architectures
2015
In this paper we present new parallelization techniques for searching large-scale biological sequence databases with the Smith-Waterman algorithm on Xeon Phi-based neoheterogenous architectures. In order to make full use of the compute power of both the multi-core CPU and the many-core Xeon Phi hardware, we use a collaborative computing scheme as well as hybrid parallelism. At the CPU side, we employ SSE intrinsics and multi-threading to implement SIMD parallelism. At the Xeon Phi side, we use Knights Corner vector instructions to gain more data parallelism. We have presented two dynamic task distribution schemes (thread level and device level) in order to achieve better load balancing. Fur…
The Dynamical Kernel Scheduler - Part 1
2015
Emerging processor architectures such as GPUs and Intel MICs provide a huge performance potential for high performance computing. However developing software using these hardware accelerators introduces additional challenges for the developer such as exposing additional parallelism, dealing with different hardware designs and using multiple development frameworks in order to use devices from different vendors. The Dynamic Kernel Scheduler (DKS) is being developed in order to provide a software layer between host application and different hardware accelerators. DKS handles the communication between the host and device, schedules task execution, and provides a library of built-in algorithms. …
Optimization of Reactive Force Field Simulation: Refactor, Parallelization, and Vectorization for Interactions
2022
Molecular dynamics (MD) simulations are playing an increasingly important role in many areas ranging from chemical materials to biological molecules. With the continuing development of MD models, the potentials are getting larger and more complex. In this article, we focus on the reactive force field (ReaxFF) potential from LAMMPS to optimize the computation of interactions. We present our efforts on refactoring for neighbor list building, bond order computation, as well as valence angles and torsion angles computation. After redesigning these kernels, we develop a vectorized implementation for non-bonded interactions, which is nearly $100 \times$ 100 × faster than the management processing…
BGSA: a bit-parallel global sequence alignment toolkit for multi-core and many-core architectures
2018
Abstract Motivation Modern bioinformatics tools for analyzing large-scale NGS datasets often need to include fast implementations of core sequence alignment algorithms in order to achieve reasonable execution times. We address this need by presenting the BGSA toolkit for optimized implementations of popular bit-parallel global pairwise alignment algorithms on modern microprocessors. Results BGSA outperforms Edlib, SeqAn and BitPAl for pairwise edit distance computations and Parasail, SeqAn and BitPAl when using more general scoring schemes for pairwise alignments of a batch of sequence reads on both standard multi-core CPUs and Xeon Phi many-core CPUs. Furthermore, banded edit distance perf…
FPGA-based Acceleration of Detecting Statistical Epistasis in GWAS
2014
Abstract Genotype-by-genotype interactions (epistasis) are believed to be a significant source of unexplained genetic variation causing complex chronic diseases but have been ignored in genome-wide association studies (GWAS) due to the computational burden of analysis. In this work we show how to benefit from FPGA technology for highly parallel creation of contingency tables in a systolic chain with a subsequent statistical test. We present the implementation for the FPGA-based hardware platform RIVYERA S6-LX150 containing 128 Xilinx Spartan6-LX150 FPGAs. For performance evaluation we compare against the method iLOCi[9]. iLOCi claims to outperform other available tools in terms of accuracy.…