Finding a novel drug is a very long and complex procedure. Using computer simulations, it is possible to accelerate the preliminary phases by performing a virtual screening that filters a large set of drug candidates to a manageable number. This paper presents the implementations and comparative analysis of two GPU-optimised implementations of a virtual screening algorithm targeting novel GPU architectures. This work focuses on the analysis of parallel computation patterns and their mapping onto the target architecture. The first method adopts a traditional approach that spreads the computation for a single molecule across the entire GPU. The second uses a novel batched approach that exploits the parallel architecture of the GPU to evaluate more molecules in parallel. Experimental results showed a different behavior depending on the size of the database to be screened, either reaching a performance plateau sooner or having a more extended initial transient period to achieve a higher throughput (up to 5x), which is more suitable for extreme-scale virtual screening campaigns.

This paper presents a GPU implementation of the graph triangle counting operation based on the set intersection algorithm. The algorithm is implemented in four kernels optimized for different types of graphs in a code delivering performance higher than the current state-of-the-art and without preprocessing the input graph. At runtime, a lightweight heuristic is used to select the kernel to run based on the specific graph taken as input. In contrast to previous works, the presented approach takes advantage of a set intersection operation implemented via bitmaps. Moreover, the simplicity of the approach allows the code to have limited size and engineering complexity.

We present the results obtained by using an evolution of our CUDA-based solution for the exploration, via a breadth first search, of large graphs. This latest version exploits at its best the features of the Kepler architecture and relies on a combination of techniques to reduce both the number of communications among the GPUs and the amount of exchanged data. The final result is a code that can visit more than 800 billion edges in a second by using a cluster equipped with 4,096 Tesla K20X GPUs.

We present simulations of blood and cancer cell separation in complex microfluidic channels with subcellular resolution, demonstrating unprecedented time to solution, performing at 65.5% of the available 39.4 PetaInstructions/s in the 18, 688 nodes of the Titan supercomputer.

We present performance results for the simulation of proteins suspensions in crowding conditions obtained with MUPHY, a computational platform for multi-scale simulations of real-life biofluidic problems. Previous versions of MUPHY have been used in the past for the simulation of blood flow through the human coronary arteries and DNA translocation across nanopores. The simulation exhibits excellent scalability up to 18, 000 K20X Nvidia GPUs and achieves almost 20 Petaflops of aggregate sustained performance with a peak performance of 27.5 Petaflops for the most intensive computing component. Those figures demonstrate once again the flexibility of MUPHY in simulating biofluidic phenomena, exploiting at their best the features of the architecture in use. Preliminary results were obtained in the present case on a completely different platform, the IBM Blue Gene/Q. The combination of novel mathematical models, computational algorithms, hardware technology, code tuning and parallelization techniques required to achieve these results are presented.

We present a computational framework for multi-scale simulations of real-life biofluidic problems. The framework allows to simulate suspensions composed by hundreds of millions of bodies interacting with each other and with a surrounding fluid in complex geometries. We apply the methodology to the simulation of blood flow through the human coronary arteries with a spatial resolution comparable with the size of red blood cells, and physiological levels of hematocrit (the red blood cell volume fraction). The simulation exhibits excellent scalability on a cluster of 4000 M2050 Nvidia GPUs and achieves close to 1 Petaflop aggregate performance, which demonstrates the capability to predicting the evolution of biofluidic phenomena of clinical significance. The combination of novel mathematical models, computational algorithms, hardware technology, code tuning and optimization required to achieve these results are presented.

We present the first large-scale simulation of blood flow in the coronary artieries and other vessels supplying blood to the heart muscle, with a realistic description of human arterial geometry at spatial resolutions from centimeters down to 10 microns (near the size of red blood cells). This multiscale simulation resolves the fluid into a billion volume units, embedded in a bounding space of 300 billion voxels, coupled with the concurrent motion of 300 million red blood cells, which interact with one another and with the surrounding fluid. The level of detail is sufficient to describe phenomena of potential physiological and clinical significance, such as the development of atherosclerotic plaques. The simulation achieves excellent scalability on up to 294, 912 Blue Gene/P computational cores.