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 $1.42-27.28\%$ and exhibit QPOs with frequency $\nu_{\rm QPO} \sim 8-667$ mHz. Long-term evolution of ULXs energy spectra ($0.3 - 10$ keV; excluding M82 X$-$1) are described satisfactorily with a model combination that comprises a thermal Comptonization component (\texttt{nthComp}, yielding $\Gamma_{\rm nth} \sim 1.48 - 2.65$, $kT_{\rm e} \sim 1.62 - 3.76$ keV, $\tau \sim 8 - 20$, y-par $\sim 1.16 - 6.24$) along with a standard disc component (\texttt{diskbb}, $kT_{\rm in} \sim 0.16 - 0.54$ keV). We find that these ULXs generally demonstrate anti-correlation between disc luminosity and inner disc temperature as $L_{\rm disc} \propto T_{\rm in}^\alpha$, where $\alpha = - 3.58 \pm 0.04$ for NGC 1313 X-1 and IC 342 X-1, $\alpha = - 8.93 \pm 0.11$ for NGC 6946 X-1, and $\alpha = - 10.31 \pm 0.10$ for NGC 5408 X-1. We also obtain a linear correlation between bolometric luminosity $L_{\rm bol}$ and $\Gamma_{\rm nth}$ that indicates spectral softening of the sources when $L_{\rm bol}$ increases. We observe that in presence of QPO, Comptonized seed photon fraction varies in between $\sim 5 - 20 \%$, while the Comptonized flux contribution ($50 - 90\%$) dominates over disc flux. Utilizing $\nu_{\rm QPO}$ and $L_{\rm bol}$, we constrain ULXs mass by varying their spin ($a_{\rm k}$) and accretion rate ($\dot m$). 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