High-throughput Binding Affinity Calculations at Extreme Scales

Resistance to chemotherapy and molecularly targeted therapies is a major factor in limiting the effectiveness of cancer treatment. In many cases, resistance can be linked to genetic changes in target proteins, either pre-existing or evolutionarily selected during treatment. Key to overcoming this challenge is an understanding of the molecular determinants of drug binding. Using multi-stage pipelines of molecular simulations we can gain insights into the binding free energy and the residence time of a ligand, which can inform both stratified and personal treatment regimes and drug development. To support the scalable, adaptive and automated calculation of the binding free energy on high-performance computing resources, we introduce the High- throughput Binding Affinity Calculator (HTBAC). HTBAC uses a building block approach in order to attain both workflow flexibility and performance. We demonstrate close to perfect weak scaling to hundreds of concurrent multi-stage binding affinity calculation pipelines. This permits a rapid time-to-solution that is essentially invariant of the calculation protocol, size of candidate ligands and number of ensemble simulations. As such, HTBAC advances the state of the art of binding affinity calculations and protocols

Resource Modification On Multicore Server With Kernel Bypass

Technology develops very fast marked by many innovations both from hardware and software. Multicore servers with a growing number of cores require efficient software. Kernel and Hardware used to handle various operational needs have some limitations. This limitation is due to the high level of complexity especially in handling as a server such as single socket discriptor, single IRQ and lack of pooling so that it requires some modifications. The Kernel Bypass is one of the methods to overcome the deficiencies of the kernel. Modifications on this server are a combination increase throughput and decrease server latency. Modifications at the driver level with hashing rx signal and multiple receives modification with multiple ip receivers, multiple thread receivers and multiple port listener used to increase throughput. Modifications using pooling principles at either the kernel level or the program level are used to decrease the latency. This combination of modifications makes the server more reliable with an average throughput increase of 250.44% and a decrease in latency 65.83%

Regions and material flows: investigating the regional branching and industry relatedness of port traffic in a global perspective

International audienceThis article proposes a quantitative analysis of the interdependencies between port specialization and regional specialization across the world. A global database is elaborated, covering about 360 port regions in both developed and developing countries. One goal is to verify how interdependent port traffic and regional characteristics are, in a context of increasingly flexible commodity and value chains. Despite the aggregated dimension of available data and the heterogeneity of local situations, the main results confirm the affinity between the primary sector and raw materials traffic, and between the tertiary sector and general cargo traffic, whereas the industrial sector offers mixed evidence. This allows us to address fundamental questions raised by both economic geography and regional science about transport and local development. The global typology of port regions points to certain regularities in their spatial distribution, and the article discusses the policy implications of particular cases

Improving network connection locality on multicore systems

Incoming and outgoing processing for a given TCP connection often execute on different cores: an incoming packet is typically processed on the core that receives the interrupt, while outgoing data processing occurs on the core running the relevant user code. As a result, accesses to read/write connection state (such as TCP control blocks) often involve cache invalidations and data movement between cores' caches. These can take hundreds of processor cycles, enough to significantly reduce performance. We present a new design, called Affinity-Accept, that causes all processing for a given TCP connection to occur on the same core. Affinity-Accept arranges for the network interface to determine the core on which application processing for each new connection occurs, in a lightweight way; it adjusts the card's choices only in response to imbalances in CPU scheduling. Measurements show that for the Apache web server serving static files on a 48-core AMD system, Affinity-Accept reduces time spent in the TCP stack by 30% and improves overall throughput by 24%.National Science Foundation (U.S.). (Grant number CNS-0834415)National Science Foundation (U.S.). (Grant number CNS-0915164)Quanta Computer (Firm

Structural Prediction of Protein–Protein Interactions by Docking: Application to Biomedical Problems

A huge amount of genetic information is available thanks to the recent advances in sequencing technologies and the larger computational capabilities, but the interpretation of such genetic data at phenotypic level remains elusive. One of the reasons is that proteins are not acting alone, but are specifically interacting with other proteins and biomolecules, forming intricate interaction networks that are essential for the majority of cell processes and pathological conditions. Thus, characterizing such interaction networks is an important step in understanding how information flows from gene to phenotype. Indeed, structural characterization of protein–protein interactions at atomic resolution has many applications in biomedicine, from diagnosis and vaccine design, to drug discovery. However, despite the advances of experimental structural determination, the number of interactions for which there is available structural data is still very small. In this context, a complementary approach is computational modeling of protein interactions by docking, which is usually composed of two major phases: (i) sampling of the possible binding modes between the interacting molecules and (ii) scoring for the identification of the correct orientations. In addition, prediction of interface and hot-spot residues is very useful in order to guide and interpret mutagenesis experiments, as well as to understand functional and mechanistic aspects of the interaction. Computational docking is already being applied to specific biomedical problems within the context of personalized medicine, for instance, helping to interpret pathological mutations involved in protein–protein interactions, or providing modeled structural data for drug discovery targeting protein–protein interactions.Spanish Ministry of Economy grant number BIO2016-79960-R; D.B.B. is supported by a predoctoral fellowship from CONACyT; M.R. is supported by an FPI fellowship from the Severo Ochoa program. We are grateful to the Joint BSC-CRG-IRB Programme in Computational Biology.Peer ReviewedPostprint (author's final draft

Multi-granular, multi-purpose and multi-Gb/s monitoring on off-the-shelf systems

This is the accepted version of the following article: [Moreno, V., Santiago del Río, P. M., Ramos, J., Muelas, D., García-Dorado, J. L., Gomez-Arribas, F. J. and Aracil, J. (2014), Multi-granular, multi-purpose and multi-Gb/s monitoring on off-the-shelf systems. Int. J. Network Mgmt., 24: 221–234. doi: 10.1002/nem.1861, which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1002/nem.1861/abstractAs an attempt to make network managers’ life easier, we present M3Omon, a system architecture that helps to develop monitoring applications and perform network diagnosis. M3Omon behaves as an intermediate layer between the traffic and monitoring applications that provides advanced features, high performance and low cost. Such advanced features leverage a multi-granular and multi-purpose approach to the monitoring problem. Multi-granular monitoring gives answer to tasks that use traffic aggregates to identify an event, and requires either flow records or packet data or even both to understand it and, eventually, take the convenient countermeasures. M3Omon provides a simple API to access traffic simultaneously at several different granularities—i.e., packet-level, flow-level and aggregate statistics. The multi-purposed design of M3Omon allows not only performing tasks in parallel that are specifically targeted to different traffic-related purposes (e.g., traffic classification and intrusion detection) but also sharing granularities between applications—e.g., several concurrent applications fed from flow records that are provided by M3Omon. Finally, the low-cost characteristic is brought by off-the-shelf systems (the combination of open-source software and commodity hardware) and the high performance is achieved thanks to modifications in the standard NIC driver, low-level hardware interaction, efficient memory management and programming optimization