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

http://wpage.unina.it/a.botta/pub/COMNET_CLOMON.pdf

Survey Cloud monitoring: A survey

As both CPUs and GPUs become employed in a wide range of applications, it has been acknowledged that both of these Processing Units (PUs) have their unique features and strengths and hence, CPU-GPU collaboration is inevitable to achieve high-performance computing. This has motivated a significant amount of research on heterogeneous computing techniques, along with the design of CPU-GPU fused chips and petascale heterogeneous supercomputers. In this article, we survey Heterogeneous Computing Techniques (HCTs) such as workload partitioning that enable utilizing both CPUs and GPUs to improve performance and/or energy efficiency. We review heterogeneous computing approaches at runtime, algorithm, programming, compiler, and application levels. Further, we review both discrete and fused CPU-GPU systems and discuss benchmark suites designed for evaluating Heterogeneous Computing Systems (HCSs). We believe that this article will provide insights into the workings and scope of applications of HCTs to researchers and motivate them to further harness the computational powers of CPUs and GPUs to achieve the goal of exascale performance.

https://dl.acm.org/doi/pdf/10.1145/2788396

A Survey of CPU-GPU Heterogeneous Computing Techniques

Scientific codes are all subject to variation in performance depending on the runtime platform and/or configuration, the output writing API employed, and the file system for output. Since changing the IO routines to match the optimal or desired configuration for a given system can be costly in terms of human time and machine resources, the Adaptable IO System provides an API nearly as simple as POSIX IO that also provides developers with the flexibility of selection the optimal IO routines for a given platform, without recompilation. As a side effect, we also gain the ability to transparently integrate more tightly with workflow systems like Kepler and Pegasus and visualization systems like Visit with no runtime impact. We achieve this through our library of highly tuned IO routines and other transport methods selected and configured in an XML file read only at startup. ADIOS-based IO has demonstrated high levels of performance and scalability. For example, we have achieved 20 GB/sec write performance using GTC on the Jaguar Cray XT4 system at Oak Ridge National Labs (about 50\% of peak performance). We can change GTC output among MPI-IO synchronous, MPI-IO collective, POSIX IO, no IO (for baseline testing), asynchronous IO using the Georgia Tech DataTap system, and Visit directly for in situ visualization with no changes to the source code. We designed this initial version of ADIOS based on the data requirements of 7 major scientific codes (GTC, Chimera, GTS, XGC1, XGC0, FLASH, and S3D) and have successfully adapted all of them to use ADIOS for all of their IO needs.

Flexible IO and integration for scientific codes through the adaptable IO system (ADIOS)

Glioblastoma is the most common and lethal primary malignant brain tumor in adults. Patients die from recurrent tumors that have become resistant to therapy. New strategies are needed to design future therapies that target resistant cells. Recent genomic studies have unveiled the complexity of tumor heterogeneity in glioblastoma and provide new insights into the genomic landscape of tumor cells that survive and initiate tumor recurrence. Resistant cells also co-opt developmental pathways and display stem-like properties; hence we propose to name them recurrence-initiating stem-like cancer (RISC) cells. Genetic alterations and genomic reprogramming underlie the innate and adaptive resistance of RISC cells, and both need to be targeted to prevent glioblastoma recurrence.

/pdf/overcoming-therapeutic-resistance-in-glioblastoma-the-way-3ge6meha1t.pdf

Overcoming therapeutic resistance in glioblastoma: the way forward

High performance computing is being increasingly utilized in non-traditional circumstances where it must interoperate with other applications For example, online visualization is being used to monitor the progress of applications, and real-world sensors are used as inputs to simulations Whenever these situations arise, there is a question of what communications infrastructure should be used to link the different components Traditional HPC-style communications systems such as MPI offer relatively high performance, but are poorly suited for developing these less tightly-coupled cooperating applications Object-based systems and meta-data formats like XML offer substantial plug-and-play flexibility, but with substantially lower performance We observe that the flexibility and baseline performance of all these systems is strongly determined by their "wire format," or how they represent data for transmission in a heterogeneous environment We examine the performance implications of different wire formats and present an alternative with significant advantages in terms of both performance and flexibility

/pdf/efficient-wire-formats-for-high-performance-computing-13dnaxx6br.pdf

Efficient Wire Formats for High Performance Computing

Today's high-end massively parallel processing (MPP) machines have thousands to tens of thousands of processors, with next-generation systems planned to have in excess of one hundred thousand processors. For systems of such scale, efficient I/O is a significant challenge that cannot be solved using traditional approaches. In particular, general purpose parallel file systems that limit applications to standard interfaces and access policies do not scale and will likely be a performance bottleneck for many scientific applications. In this paper, we investigate the use of a "lightweight" approach to I/O that requires the application or I/O-library developer to extend a core set of critical I/O functionality with the minimum set of features and services required by its target applications. We argue that this approach allows the development of I/O libraries that are both scalable and secure. We support our claims with preliminary results for a lightweight checkpoint operation on a development cluster at Sandia.

/pdf/lightweight-i-o-for-scientific-applications-8fgeudmkkx.pdf

Lightweight I/O for Scientific Applications

The integration of imaging and genomic data is critical to forming a better understanding of disease. Large public datasets, such as The Cancer Genome Atlas, present a unique opportunity to integrate these complementary data types for in silico scientific research. In this letter, we focus on the aspect of pathology image analysis and illustrate the challenges associated with analyzing and integrating large-scale image datasets with molecular characterizations. We present an example study of diffuse glioma brain tumors, where the morphometric analysis of 81 million nuclei is integrated with clinically relevant transcriptomic and genomic characterizations of glioblastoma tumors. The preliminary results demonstrate the potential of combining morphometric and molecular characterizations for in silico research.

An Integrative Approach for In Silico Glioma Research

We explore the use of compression methods to improve the middleware-based exchange of information in interactive or collaborative distributed applications. In such applications, good compression factors must be accompanied by compression speeds suitable for the data transfer rates sustainable across network links. Our approach combines methods that continuously monitor current network and processor resources and assess compression effectiveness, with techniques that automatically choose suitable compression techniques. By integrating these techniques into middleware, there is little need for end user involvement, other than expressing the target rates of data transmission. The resulting network- and user-aware compression methods are evaluated experimentally across a range of network links and application data, the former ranging from low end links to homes, to wide-area Internet links, to high end links in intranets, the latter including both scientific (binary molecular dynamics data) and commercial (XML) data sets. Results attained demonstrate substantial improvements of this adaptive technique for data compression over non-adaptive approaches, where better compression methods are used when CPU loads are low and/or network links are slow, and where less effective and typically, faster compression techniques are used in high end network infrastructures.

/pdf/efficient-end-to-end-data-exchange-using-configurable-121gvudqct.pdf

Efficient end to end data exchange using configurable compression

We explore the use of compression methods to improve the middleware-based exchange of information in interactive or collaborative distributed applications. In such applications, good compression factors must be accompanied by compression speeds suitable for the data transfer rates sustainable across network links. Our approach combines methods that continuously monitor current network and processor resources and assess compression effectiveness, with techniques that automatically choose suitable compression techniques. The resulting network- and user-aware compression methods are evaluated experimentally across a range of network links and application data, the former ranging from low end links to homes, to wide-area Internet links, to high end links in intranets, the latter including both scientific (binary molecular dynamics data) and commercial (XML) data sets. Results attained demonstrate substantial improvements of this adaptive technique for data compression over non-adaptive approaches, where better compression methods are used when CPU loads are low and/or network links are slow, and where less effective and typically, faster compression techniques are used in high end network infrastructures.

Patrick Widener

Papers

Efficient Wire Formats for High Performance Computing

Lightweight I/O for Scientific Applications

An Integrative Approach for In Silico Glioma Research

Efficient end to end data exchange using configurable compression

Efficient end to end data exchange using configurable compression