Supporting Service Differentiation with Enhancements of the IEEE 802.11 MAC Protocol: Models and Analysis

As one of the fastest growing wireless access technologies, Wireless LANs must evolve to support adequate degrees of service differentiation. Unfortunately, current WLAN standards like IEEE 802.11 Distributed Coordination Function (DCF) lack this ability. Work is in progress to define an enhanced version capable of supporting QoS for multimedia traffic at the MAC layer. In this paper, we aim at gaining insight into three mechanisms to differentiate among traffic categories, i.e., differentiating the minimum contention window size, the Inter-Frame Spacing (IFS) and the length of the packet payload according to the priority of different traffic categories. We propose an analysis model to compute the throughput and packet transmission delays. In additions, we derive approximations to get simpler but more meaningful relationships among different parameters. Comparisons with discrete-event simulation results show that a very good accuracy of performance evaluation can be achieved by using the proposed analysis model

Analytical calculation of pressure for confined atomic and molecular systems using the eXtreme-Pressure Polarizable Continuum Model

We show that the pressure acting on atoms and molecular systems within the compression cavity of the eXtreme-Pressure Polarizable Continuum method can be expressed in terms of the electron density of the systems and of the Pauli-repulsion confining potential. The analytical expression holds for spherical cavities as well as for cavities constructed from van der Waals spheres of the constituting atoms of the molecular systems

Preparing HPC Applications for the Exascale Era: A Decoupling Strategy

Production-quality parallel applications are often a mixture of diverse operations, such as computation- and communication-intensive, regular and irregular, tightly coupled and loosely linked operations. In conventional construction of parallel applications, each process performs all the operations, which might result inefficient and seriously limit scalability, especially at large scale. We propose a decoupling strategy to improve the scalability of applications running on large-scale systems. Our strategy separates application operations onto groups of processes and enables a dataflow processing paradigm among the groups. This mechanism is effective in reducing the impact of load imbalance and increases the parallel efficiency by pipelining multiple operations. We provide a proof-of-concept implementation using MPI, the de-facto programming system on current supercomputers. We demonstrate the effectiveness of this strategy by decoupling the reduce, particle communication, halo exchange and I/O operations in a set of scientific and data-analytics applications. A performance evaluation on 8,192 processes of a Cray XC40 supercomputer shows that the proposed approach can achieve up to 4x performance improvement.Comment: The 46th International Conference on Parallel Processing (ICPP-2017

Exploring Application Performance on Emerging Hybrid-Memory Supercomputers

Next-generation supercomputers will feature more hierarchical and heterogeneous memory systems with different memory technologies working side-by-side. A critical question is whether at large scale existing HPC applications and emerging data-analytics workloads will have performance improvement or degradation on these systems. We propose a systematic and fair methodology to identify the trend of application performance on emerging hybrid-memory systems. We model the memory system of next-generation supercomputers as a combination of "fast" and "slow" memories. We then analyze performance and dynamic execution characteristics of a variety of workloads, from traditional scientific applications to emerging data analytics to compare traditional and hybrid-memory systems. Our results show that data analytics applications can clearly benefit from the new system design, especially at large scale. Moreover, hybrid-memory systems do not penalize traditional scientific applications, which may also show performance improvement.Comment: 18th International Conference on High Performance Computing and Communications, IEEE, 201

Idle Period Propagation in Message-Passing Applications

Idle periods on different processes of Message Passing applications are unavoidable. While the origin of idle periods on a single process is well understood as the effect of system and architectural random delays, yet it is unclear how these idle periods propagate from one process to another. It is important to understand idle period propagation in Message Passing applications as it allows application developers to design communication patterns avoiding idle period propagation and the consequent performance degradation in their applications. To understand idle period propagation, we introduce a methodology to trace idle periods when a process is waiting for data from a remote delayed process in MPI applications. We apply this technique in an MPI application that solves the heat equation to study idle period propagation on three different systems. We confirm that idle periods move between processes in the form of waves and that there are different stages in idle period propagation. Our methodology enables us to identify a self-synchronization phenomenon that occurs on two systems where some processes run slower than the other processes.Comment: 18th International Conference on High Performance Computing and Communications, IEEE, 201

Exploring the Performance Benefit of Hybrid Memory System on HPC Environments

Hardware accelerators have become a de-facto standard to achieve high performance on current supercomputers and there are indications that this trend will increase in the future. Modern accelerators feature high-bandwidth memory next to the computing cores. For example, the Intel Knights Landing (KNL) processor is equipped with 16 GB of high-bandwidth memory (HBM) that works together with conventional DRAM memory. Theoretically, HBM can provide 5x higher bandwidth than conventional DRAM. However, many factors impact the effective performance achieved by applications, including the application memory access pattern, the problem size, the threading level and the actual memory configuration. In this paper, we analyze the Intel KNL system and quantify the impact of the most important factors on the application performance by using a set of applications that are representative of scientific and data-analytics workloads. Our results show that applications with regular memory access benefit from MCDRAM, achieving up to 3x performance when compared to the performance obtained using only DRAM. On the contrary, applications with random memory access pattern are latency-bound and may suffer from performance degradation when using only MCDRAM. For those applications, the use of additional hardware threads may help hide latency and achieve higher aggregated bandwidth when using HBM

Extending Message Passing Interface Windows to Storage

This work presents an extension to MPI supporting the one-sided communication model and window allocations in storage. Our design transparently integrates with the current MPI implementations, enabling applications to target MPI windows in storage, memory or both simultaneously, without major modifications. Initial performance results demonstrate that the presented MPI window extension could potentially be helpful for a wide-range of use-cases and with low-overhead

Stiffening and unfolding of early deposited-fibronectin increase proangiogenic factor secretion by breast cancer-associated stromal cells.

Fibronectin (Fn) forms a fibrillar network that controls cell behavior in both physiological and diseased conditions including cancer. Indeed, breast cancer-associated stromal cells not only increase the quantity of deposited Fn but also modify its conformation. However, (i) the interplay between mechanical and conformational properties of early tumor-associated Fn networks and (ii) its effect on tumor vascularization remain unclear. Here, we first used the Surface Forces Apparatus to reveal that 3T3-L1 preadipocytes exposed to tumor-secreted factors generate a stiffer Fn matrix relative to control cells. We then show that this early matrix stiffening correlates with increased molecular unfolding in Fn fibers, as determined by Förster Resonance Energy Transfer. Finally, we assessed the resulting changes in adhesion and proangiogenic factor (VEGF) secretion of newly seeded 3T3-L1s, and we examined altered integrin specificity as a potential mechanism of modified cell-matrix interactions through integrin blockers. Our data indicate that tumor-conditioned Fn decreases adhesion while enhancing VEGF secretion by preadipocytes, and that an integrin switch is responsible for such changes. Collectively, our findings suggest that simultaneous stiffening and unfolding of initially deposited tumor-conditioned Fn alters both adhesion and proangiogenic behavior of surrounding stromal cells, likely promoting vascularization and growth of the breast tumor. This work enhances our knowledge of cell - Fn matrix interactions that may be exploited for other biomaterials-based applications, including advanced tissue engineering approaches