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

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

Ethnomedical survey of plants used by the Orang Asli in Kampung Bawong, Perak, West Malaysia

<p>Abstract</p> <p>Background</p> <p>A qualitative ethnomedical survey was carried out among a local Orang Asli tribe to gather information on the use of medicinal plants in the region of Kampung Bawong, Perak of West Malaysia in order to evaluate the potential medicinal uses of local plants used in curing different diseases and illnesses.</p> <p>Methods</p> <p>Sixteen informants ranging in age from 35 to 65 years were interviewed. A total of 62 species of plants used by Orang Asli are described in this study based on field surveys and direct face to face communication. These plants belonged to 36 families and are used to treat a wide range of discomforts and diseases.</p> <p>Results</p> <p>The results of this study showed that majority of the Orang Asli, of Kampung Bawong are still dependent on local plants as their primary source of medication. As the first ethnomedical study in this area, publishing this work is expected to open up more studies to identify and assess the pharmacological and toxicological action of the plants from this region.</p> <p>Conclusions</p> <p>Preservation and recording of ethnobotanical and ethnomedical uses of traditional medicinal plants is an indispensable obligation for sustaining the medicinal and cultural resource of mankind. Extensive research on such traditional plants is of prime importance to scientifically validate their ethnomedical claims.</p

The Blacklisting Memory Scheduler: Balancing Performance, Fairness and Complexity

<p>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 interference, by ranking individual applications based on their memory access characteristics and enforcing a total rank order.</p> <p>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 individually with a total order based on memory access characteristics leads to high hardware cost and complexity. Such complexity could prevent the scheduler from meeting the stringent timing requirements of state-of-the-art DDR protocols. Second, ranking can unfairly slow down applications that are at the bottom of the ranking stack, thereby sometimes leading to high slowdowns and low overall system performance. To overcome these shortcomings, we propose the Blacklisting Memory Scheduler (BLISS), which achieves high system performance and fairness while incurring low hardware cost and complexity. BLISS design is based on two new observations. First, we find that, to mitigate interference, it is sufficient to separate applications into only two groups, one containing applications that are vulnerable to interference and another containing applications that cause interference, instead of ranking individual applications with a total order. Vulnerable-to-interference group is prioritized over the interference-causing group. Second, we show that this grouping can be efficiently performed by simply counting the number of consecutive requests served from each application – an application that has a large number of consecutive requests served is dynamically classified as interference-causing.</p> <p>We evaluate BLISS across a wide variety of workloads and system configurations and compare its performance and hardware complexity (via RTL implementations), 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 memory 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.</p