1,865 research outputs found
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
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