78 research outputs found
The Blacklisting Memory Scheduler: Balancing Performance, Fairness and Complexity
In a multicore system, applications running on different cores interfere at
main memory. This inter-application interference degrades overall system
performance and unfairly slows down applications. Prior works have developed
application-aware memory schedulers to tackle this problem. State-of-the-art
application-aware memory schedulers prioritize requests of applications that
are vulnerable to interference, by ranking individual applications based on
their memory access characteristics and enforcing a total rank order.
In this paper, we observe that state-of-the-art application-aware memory
schedulers have two major shortcomings. First, such schedulers trade off
hardware complexity in order to achieve high performance or fairness, since
ranking applications with a total order leads to high hardware complexity.
Second, ranking can unfairly slow down applications that are at the bottom of
the ranking stack. To overcome these shortcomings, we propose the Blacklisting
Memory Scheduler (BLISS), which achieves high system performance and fairness
while incurring low hardware complexity, based on two observations. First, we
find that, to mitigate interference, it is sufficient to separate applications
into only two groups. Second, we show that this grouping can be efficiently
performed by simply counting the number of consecutive requests served from
each application.
We evaluate BLISS across a wide variety of workloads/system configurations
and compare its performance and hardware complexity, with five state-of-the-art
memory schedulers. Our evaluations show that BLISS achieves 5% better system
performance and 25% better fairness than the best-performing previous scheduler
while greatly reducing critical path latency and hardware area cost of the
memory scheduler (by 79% and 43%, respectively), thereby achieving a good
trade-off between performance, fairness and hardware complexity
Unveiling the accretion scenario of BH-ULXs using XMM-Newton observations
We present a comprehensive spectro-temporal analysis of five ultraluminous
X-ray sources (ULXs) with central object likely being a black hole, using
archival {\it XMM-Newton} observations. These sources, namely NGC 1313 X-1, NGC
5408 X-1, NGC 6946 X-1, M82 X-1 and IC 342 X-1, reveal short-term variability
with fractional variance of and exhibit QPOs with frequency
mHz. Long-term evolution of ULXs energy spectra
( keV; excluding M82 X1) are described satisfactorily with a model
combination that comprises a thermal Comptonization component
(\texttt{nthComp}, yielding , keV, , y-par ) along with
a standard disc component (\texttt{diskbb},
keV). We find that these ULXs generally demonstrate anti-correlation between
disc luminosity and inner disc temperature as , where for NGC 1313 X-1 and IC 342 X-1,
for NGC 6946 X-1, and
for NGC 5408 X-1. We also obtain a linear correlation between bolometric
luminosity and that indicates spectral
softening of the sources when increases. We observe that in
presence of QPO, Comptonized seed photon fraction varies in between , while the Comptonized flux contribution () dominates over
disc flux. Utilizing and , we constrain ULXs mass
by varying their spin () and accretion rate (). We find that
NGC 6946 X-1 and NGC 5408 X-1 seem to accrete at sub-Eddington accretion rate
provided their central sources are rapidly rotating, whereas IC 342 X-1 and NGC
1313 X-1 can accrete in sub/super-Eddington limit irrespective to their spin
values.Comment: 21 pages, 11 figures, 6 tables, accepted for publication in MNRA
The Blacklisting Memory Scheduler: Achieving high performance and fairness at low cost
Abstract—In a multicore system, applications running on different cores interfere at main memory. This inter-application interference degrades overall system performance and unfairly slows down applications. Prior works have developed application-aware memory request schedulers to tackle this problem. State-of-the-art application-aware memory request schedulers prioritize memory requests of applications that are vulnerable to interfer-ence, by ranking individual applications based on their memory access characteristics and enforcing a total rank order. In this paper, we observe that state-of-the-art application-aware memory schedulers have two major shortcomings. First, ranking applications individually with a total order based on memory access characteristics leads to high hardware cost and complexity. Second, ranking can unfairly slow down applications that are at the bottom of the ranking stack. To overcome thes
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