Bioinformatics and Computational Biology (BCB) is a relatively new multidisciplinary field which brings together many aspects of the fields of biology, computer science, statistics, and engineering. Bioinformatics extracts useful information from biological data and makes these more intuitive and understandable by applying principles of information sciences, while computational biology harnesses computational approaches and technologies to answer biological questions conveniently. Recent years have seen an explosion of the size of biological data at a rate which outpaces the rate of increases in the computational power of mainstream computer technologies, namely general purpose processors (GPPs). The aim of this thesis is to explore the use of off-the-shelf Graphics Processing Unit (GPU) technology in the high performance and efficient implementation of BCB applications in order to meet the demands of biological data increases at affordable cost. The thesis presents detailed design and implementations of GPU solutions for a number of BCB algorithms in two widely used BCB applications, namely biological sequence alignment and phylogenetic analysis. Biological sequence alignment can be used to determine the potential information about a newly discovered biological sequence from other well-known sequences through similarity comparison. On the other hand, phylogenetic analysis is concerned with the investigation of the evolution and relationships among organisms, and has many uses in the fields of system biology and comparative genomics. In molecular-based phylogenetic analysis, the relationship between species is estimated by inferring the common history of their genes and then phylogenetic trees are constructed to illustrate evolutionary relationships among genes and organisms. However, both biological sequence alignment and phylogenetic analysis are computationally expensive applications as their computing and memory requirements grow polynomially or even worse with the size of sequence databases. The thesis firstly presents a multi-threaded parallel design of the Smith- Waterman (SW) algorithm alongside an implementation on NVIDIA GPUs. A novel technique is put forward to solve the restriction on the length of the query sequence in previous GPU-based implementations of the SW algorithm. Based on this implementation, the difference between two main task parallelization approaches (Inter-task and Intra-task parallelization) is presented. The resulting GPU implementation matches the speed of existing GPU implementations while providing more flexibility, i.e. flexible length of sequences in real world applications. It also outperforms an equivalent GPPbased implementation by 15x-20x. After this, the thesis presents the first reported multi-threaded design and GPU implementation of the Gapped BLAST with Two-Hit method algorithm, which is widely used for aligning biological sequences heuristically. This achieved up to 3x speed-up improvements compared to the most optimised GPP implementations. The thesis then presents a multi-threaded design and GPU implementation of a Neighbor-Joining (NJ)-based method for phylogenetic tree construction and multiple sequence alignment (MSA). This achieves 8x-20x speed up compared to an equivalent GPP implementation based on the widely used ClustalW software. The NJ method however only gives one possible tree which strongly depends on the evolutionary model used. A more advanced method uses maximum likelihood (ML) for scoring phylogenies with Markov Chain Monte Carlo (MCMC)-based Bayesian inference. The latter was the subject of another multi-threaded design and GPU implementation presented in this thesis, which achieved 4x-8x speed up compared to an equivalent GPP implementation based on the widely used MrBayes software. Finally, the thesis presents a general evaluation of the designs and implementations achieved in this work as a step towards the evaluation of GPU technology in BCB computing, in the context of other computer technologies including GPPs and Field Programmable Gate Arrays (FPGA) technology.

This work endeavours to achieve technology transfer between established best-practice in academic high-performance computing and current techniques in commercial high-productivity computing. It shows that a credible high-performance message-passing communication library, with semantics and syntax following the Message-Passing Interface (MPI) Standard, can be built in pure C# (one of the .Net suite of computer languages). Message-passing has been the dominant paradigm in high-performance parallel programming of distributed-memory computer architectures for three decades. The MPI Standard originally distilled architecture-independent and language-agnostic ideas from existing specialised communication libraries and has since been enhanced and extended. Object-oriented languages can increase programmer productivity, for example by allowing complexity to be managed through encapsulation. Both the C# computer language and the .Net common language runtime (CLR) were originally developed by Microsoft Corporation but have since been standardised by the European Computer Manufacturers Association (ECMA) and the International Standards Organisation (ISO), which facilitates portability of source-code and compiled binary programs to a variety of operating systems and hardware. Combining these two open and mature technologies enables mainstream programmers to write tightly-coupled parallel programs in a popular standardised object-oriented language that is portable to most modern operating systems and hardware architectures. This work also establishes that a thread-to-thread delivery option increases shared-memory communication performance between MPI ranks on the same node. This suggests that the thread-as-rank threading model should be explicitly specified in future versions of the MPI Standard and then added to existing MPI libraries for use by thread-safe parallel codes. This work also ascertains that the C# socket object suffers from undesirable characteristics that are critical to communication performance and proposes ways of improving the implementation of this object.

La méthode d’imagerie la plus répandue dans l’industrie pétrolière est la RTM (Reverse Time Migration) qui repose sur la simulation de la propagation des ondes dans le sous-sol. Nous nous sommes concentrés sur un propagateur d'ondes élastiques 3D en milieu anisotrope de type TTI (Tilted Transverse Isotropic). Nous avons directement travaillé dans le code de recherche de Total DIVA (Depth Imaging Velocity Analysis), basé sur une discrétisation par la méthode de Galerkin Discontinue et le schéma Leap-Frog, et développé pour le calcul parallèle intensif – HPC (High Performance Computing). Nous avons ciblé plus particulièrement deux contributions possibles qui, si elles supposent des compétences très différentes, ont la même finalité : réduire les coûts de calculs requis pour la simulation. D'une part, les conditions aux limites classiques de type PML (Perfectly Matched Layers) ne sont pas stables dans des milieux TTI. Nous avons proposé de formuler une CLA (Conditions aux Limites Absorbantes) stable dans des milieux anisotropes. La méthode de construction repose sur les propriétés des courbes de lenteur, ce qui donne à notre approche un caractère original. D'autre part, le parallélisme initial, basé sur une décomposition de domaine et des communications par passage de messages à l'aide de la bibliothèque MPI, conduit à un déséquilibrage de charge qui détériore son efficacité parallèle. Nous avons corrigé cela en remplaçant le paradigme parallélisme par l'utilisation de la programmation à base de tâches sur support d'exécution. Cette thèse a été réalisée dans le cadre de l'action de recherche DIP (Depth Imaging Partnership) qui lie la compagnie pétrolière Total et Inria. / The most common method of Seismic Imaging is the RTM (Reverse Time Migration) which depends on wave propagation simulations in the subsurface. We focused on a 3D elastic wave propagator in anisotropic media, more precisely TTI (Tilted Transverse Isotropic). We directly worked in the Total code DIVA (Depth Imaging Velocity Analysis) which is based on a discretization by the Discontinuous Galerkin method and the Leap-Frog scheme, and developed for intensive parallel computing – HPC (High Performance Computing). We choose to especially target two contributions. Although they required very different skills, they share the same goal: to reduce the computational cost of the simulation. On one hand, classical boundary conditions like PML (Perfectly Matched Layers) are unstable in TTI media. We have proposed a formulation of a stable ABC (Absorbing Boundary Condition) in anisotropic media. The technique is based on slowness curve properties, giving to our approach an original side. On the other hand, the initial parallelism, which is based on a domain decomposition and communications by message passing through the MPI library, leads to load-imbalance and so poor parallel efficiency. We have fixed this issue by replacing the paradigm for parallelism by the use of task-based programming through runtime system. This PhD thesis have been done in the framework of the research action DIP (Depth Imaging Partnership) between the Total oil company and Inria.