Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

Fast parallel algorithms for short-range molecular dynamics

What is Twitter

Dynamics of information diffusion and its applications on complex networks

Social media data have been extensively used in numerous fields of science, but examples of their use in conservation science are still very limited In this paper, we propose a framework on how social media data could be useful for conservation science and practice We present the commonly used social media platforms and discuss how their content could be providing new data and information for conservation science Based on this, we discuss how future work in conservation science and practice would benefit from social media data

/pdf/prospects-and-challenges-for-social-media-data-in-120hetlqsw.pdf

Prospects and challenges for social media data in conservation science

Using semantic technologies for mining and intelligent information access to social media is a challenging, emerging research area. Traditional search methods are no longer able to address the more complex information seeking behaviour in media streams, which has evolved towards sense making, learning, investigation, and social search. Unlike carefully authored news text and longer web context, social media streams pose a number of new challenges, due to their large-scale, short, noisy, contextdependent, and dynamic nature. This paper defines five key research questions in this new application area, examined through a survey of state-of-the-art approaches to mining semantics from social media streams; user, network, and behaviour modelling; and intelligent, semanticbased information access. The survey includes key methods not just from the Semantic Web research field, but also from the related areas of natural language processing and user modelling. In conclusion, key outstanding challenges are discussed and new directions for research are proposed.

/pdf/making-sense-of-social-media-streams-through-semantics-a-qye1c9o8jy.pdf

Making Sense of Social Media Streams through Semantics: a Survey

We present the first comprehensive characterization of the diffusion of ideas on Twitter, studying more than 5.96 million topics that include both popular and less popular topics. On a data set containing approximately 10 million users and a comprehensive scraping of 196 million tweets, we perform a rigorous temporal and spatial analysis, investigating the time-evolving properties of the subgraphs formed by the users discussing each topic. We focus on two different notions of the spatial: the network topology formed by follower-following links on Twitter, and the geospatial location of the users. We investigate the effect of initiators on the popularity of topics and find that users with a high number of followers have a strong impact on topic popularity. We deduce that topics become popular when disjoint clusters of users discussing them begin to merge and form one giant component that grows to cover a significant fraction of the network. Our geospatial analysis shows that highly popular topics are those that cross regional boundaries aggressively.

/pdf/spatio-temporal-and-events-based-analysis-of-topic-2dn8e843t8.pdf

Spatio-temporal and events based analysis of topic popularity in twitter

We present the first comprehensive characterization of the diffusion of ideas on Twitter, studying more than 4000 topics that include both popular and less popular topics. On a data set containing approximately 10 million users and a comprehensive scraping of all the tweets posted by these users between June 2009 and August 2009 (approximately 200 million tweets), we perform a rigorous temporal and spatial analysis, investigating the time-evolving properties of the subgraphs formed by the users discussing each topic. We focus on two different notions of the spatial: the network topology formed by follower-following links on Twitter, and the geospatial location of the users. We investigate the effect of initiators on the popularity of topics and find that users with a high number of followers have a strong impact on popularity. We deduce that topics become popular when disjoint clusters of users discussing them begin to merge and form one giant component that grows to cover a significant fraction of the network. Our geospatial analysis shows that highly popular topics are those that cross regional boundaries aggressively.

Spatio-Temporal Analysis of Topic Popularity in Twitter

The overlap of computation and communication is critical for good performance of many HPC applications. State-of-the-art designs for the asynchronous progress require specially designed hardware resources (advanced switches or network interface cards), dedicated processor cores or application modification (e.g. use of MPI_Test). These techniques suffer from various issues like increasing code complexity/cost and loss of available compute resources for end applications. In this paper, we take up this challenge and propose a simple yet effective technique to achieve good overlap without needing any additional hardware or software resources. The proposed thread-based design allows MPI libraries to self-detect when asynchronous communication progress is needed and minimizes the number of context-switches and preemption between the main thread and the asynchronous progress thread. We evaluate the proposed design against state-of-the-art designs in other MPI libraries including MVAPICH2, Intel MPI, and Open MPI. We demonstrate benefits of the proposed approach at microbenchmark and at application level at scale on four different architectures including Intel Broadwell, Intel Xeon Phi (KNL), IBM OpenPOWER, and Intel Skylake with InfiniBand and Omni-Path interconnects. Compared to other state-of-the-art designs, our proposed approach shows upto 46%, 37%, and 49% improvement for All-to-one, One-to-all, and All-to-all communication patterns respectively collectives on 1,024 processes. We also show 38% performance improvement for SPEC MPI compute-intensive applications on 384 processes and 44% performance improvement with the P3DFFT application on 448 processes.

Efficient Asynchronous Communication Progress for MPI without Dedicated Resources

The CUDA Unified Memory (UM) interface enables a significantly simpler programming paradigm and has the potential to fundamentally change the way programmers write CUDA applications in the future. Although UM leads to high productivity in programming using CUDA by simplifying the programmer's view of CPU and GPU memory spaces, initial support for UM in the Kepler series of GPUs lacked in performance necessitating several UM-aware designs in state-of-the-art MPI runtimes such as MVAPICH2-GDR. This has enabled end MPI applications to take advantage of the high productivity promised by UM along with high performance. However, as CUDA runtimes and GPU architectures advance, the performance offered by UM has also improved significantly. Thus, there is a need to re-evaluate the performance characteristics of UM in light of these changes to understand how the UM-aware designs in state-of-the-art MPI runtimes must be adapted. We take up this broad challenge and characterize the performance of UM-aware MPI operations and to gain insights on how MPI runtimes need to deal with UM-based data residing on GPU and CPU for different generations of GPU architectures. Our characterization studies show that UM designs conceived during the Kepler GPU era still stands valid and provide valuable performance improvement on the latest Pascal and Volta GPUs. Furthermore, performance evaluation of optimized UM designs show that they outperform naive designs on MVAPICH2-GDR and Open MPI by 4.2x and 2.8x respectively for Intel systems. Additionally, the DD experiments for pure device transfers also show that MVAPICH2-GDR is up to 12.6x better than OpenMPI (w/ UCX).

Characterizing CUDA Unified Memory (UM)-Aware MPI Designs on Modern GPU Architectures

The desire for high performance on scalable parallel systems is increasing the complexity and tunability of MPI implementations. The MPI Tools Information Interface (MPI_T) introduced as part of the MPI 3.0 standard provides an opportunity for performance tools and external software to introspect and understand MPI runtime behavior at a deeper level to detect scalability issues. The interface also provides a mechanism to fine-tune the performance of the MPI library dynamically at runtime. In this paper, we propose an infrastructure that extends existing components — TAU, MVAPICH2, and BEACON to take advantage of the MPI_T interface and offer runtime introspection, online monitoring, recommendation generation, and autotuning capabilities. We validate our design by developing optimizations for a combination of production and synthetic applications. Using our infrastructure, we implement an autotuning policy for AmberMD (a molecular dynamics package) that monitors and reduces the internal memory footprint of the MVAPICH2 MPI library without affecting performance. For applications such as MiniAMR whose collective communication is latency sensitive, our infrastructure is able to generate recommendations to enable hardware offloading of collectives supported by MVAPICH2. By implementing this recommendation, the MPI time for MiniAMR at 224 processes reduces by 15%.

Amit Ruhela

Papers

Spatio-temporal and events based analysis of topic popularity in twitter

Spatio-Temporal Analysis of Topic Popularity in Twitter

Efficient Asynchronous Communication Progress for MPI without Dedicated Resources

Characterizing CUDA Unified Memory (UM)-Aware MPI Designs on Modern GPU Architectures

MPI performance engineering with the MPI tool interface: The integration of MVAPICH and TAU