  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.
