Production systems run complex stacks comprising constantly-evolving hardware and software components. Vulnerabilities in such stacks continuously pose security risks to both the service provider and customers, thus calling for a solution to analyze and quantify security risks. STRonG is a framework that leverages a layered graph-based approach to model, analyze, and quantify security risks in complex software and hardware stacks of systems. We propose using adjustable templates/stencils for relatively tractable and consistent modeling and allow user-defined scoring methods to be applied. STRonG quantitatively assesses how structure, components, or attribute modifications impact the security risk of critical parts of a system stack during the design or early stages of the development process. The framework’s efficacy is demonstrated by applying STRonG to the control stack of OpenStack cloud infrastructure and performing risk assessment before and after introducing a novel security layer, Secure Hypervisor Channel (SHC). We also demonstrate how introducing SHC can quantitatively reduce system risk.

Computational models can define the functional dynamics of complex systems in exceptional detail. However, many modeling studies face seemingly incommensurate requirements: to gain meaningful insights into some phenomena requires models with high resolution (microscopic) detail that must nevertheless evolve over large (macroscopic) length- and time-scales. Multiscale modeling has become increasingly important to bridge this gap. Executing complex multiscale models on current petascale computers with high levels of parallelism and heterogeneous architectures is challenging. Many distinct types of resources need to be simultaneously managed, such as GPUs and CPUs, memory size and latencies, communication bottlenecks, and filesystem bandwidth. In addition, robustness to failure of compute nodes, network, and filesystems is critical.

A graph application written using a distributed graph processing framework can perform over an order of magnitude slower than its high-performance, native counterpart. This issue stems from the aim, common to most graph frameworks, of restricting the scope of application development to specific graph constructs, such as, for example, vertex or edge programs.

Scientific workflows are often composed of compute-intensive simulations and data-intensive analysis and visualization, both equally important for productivity. High-performance computers run the compute-intensive phases efficiently, but data-intensive processing is still getting less attention. Dense non-volatile memory integrated into super-computers can help address this problem. In addition to density, it offers significantly finer-grained I/O than disk-based I/O systems. We present a way to exploit the fundamental capabilities of Storage-Class Memories (SCM), such as Flash, by using scalable key-value (KV) I/O methods instead of traditional file I/O calls commonly used in HPC systems. Our objective is to enable higher performance for on-line and near-line storage for analysis and visualization of very high resolution, but correspondingly transient, simulation results. In this paper, we describe 1) the adaptation of a scalable key-value store to a BlueGene/Q system with integrated Flash memory, 2) a novel key-value aggregation module which implements coalesced, function-shipped calls between the clients and the servers, and 3) the refactoring of a scientific workflow to use application-relevant keys for fine-grained data subsets. The resulting implementation is analogous to function-shipping of POSIX I/O calls but shows an order of magnitude increase in read and a factor 2.5x increase in write IOPS performance (11 million read IOPS, 2.5 million write IOPS from 4096 compute nodes) when compared to a classical file system on the same system. It represents an innovative approach for the integration of SCM within an HPC system at scale.

GAMMA (the Genoa active message machine) is a lightweight messaging system for fast and Gigabit Ethernet. It is based on an active message-like paradigm and provides a performing, cost-effective alternative to proprietary high-speed networks, e.g. Myrinet, with the combination of low end-to-end latency and high throughput. GAMMA supports the important class of MPI based parallel applications via the MPI/GAMMA interface [G. Ciaccio], but up to now support for the also important class of socket based cluster applications is still missing. This paper describes two different approaches to how the socket interface can be adapted to GAMMA: The first is transparent for both application and GAMMA layer, the second is only transparent for the application. First performance results with the so-called GAMMA sockets are given. They show that the second approach performs almost as good as native GAMMA communication.