Abstract
The Poisson-Boltzmann equation models the electrostatic potential generated by fixed charges on a polarizable solute immersed in an ionic solution. This approach is often used in computational structural biology to estimate the electrostatic energetic component of the assembly of molecular biological systems. In the last decades, the amount of data concerning proteins and other biological macromolecules has remarkably increased. To fruitfully exploit these data, a huge computational power is needed as well as software tools capable of exploiting it. It is therefore necessary to move towards high performance computing and to develop proper parallel implementations of already existing and of novel algorithms. Nowadays, workstations can provide an amazing computational power: up to 10 TFLOPS on a single machine equipped with multiple CPUs and accelerators such as Intel Xeon Phi or GPU devices. The actual obstacle to the full exploitation of modern heterogeneous resources is efficient parallel coding and porting of software on such architectures. In this paper, we propose the implementation of a full Poisson-Boltzmann solver based on a finite-difference scheme using different and combined parallel schemes and in particular a mixed MPI-CUDA implementation. Results show great speedups when using the two schemes, achieving an 18.9x speedup using three GPUs.
Highlights
The Poisson-Boltzmann equation (PBE) describes the electrostatic behavior of a polarizable solute containing fixed charges immersed in an ionic and polarizable solution. It is a popular and effective model adopted in the computational structural biology and biophysics communities, where the estimate of the electrostatic energy of molecular systems is used, for instance, to study stability, binding affinity, desolvation penalty, acid constants, and so forth [1, 2]
We present an implementation of a full PBE solver based on a finite-difference (FD) scheme using different parallelization schemes and in particular a combined MPI-compute unified device architecture (CUDA) implementation
Presenting the results achieved with the CUDA implementation, to enable a fair comparison of the results obtained on the different clusters, we do not present the time spent in MPI communications and we report the values achieved on the single iteration of the solver
Summary
The Poisson-Boltzmann equation (PBE) describes the electrostatic behavior of a polarizable solute containing fixed charges immersed in an ionic and polarizable solution. Researchers started using the same approaches once devised for small sized proteins to more complex and larger scale systems, such as multimeric receptors in cell membrane, virus capsids, and ribosomes [5] This is instrumental to the creation of databases of paired experimental and simulated data necessary to a better comprehension of biological and medical data. The last generation CPUs can access powerful specialized hardware, as general-purpose graphics processing units (GPGPUs, shortly, GPUs) and field-programmable gate arrays (FPGAs) [8] These emerging heterogeneous HPC architectures provide significant computational power, in the order of TFLOPS. This raw potential can become practically available only through a massive exploitation of parallelism, which requires a tailored approach for each architecture.
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