235,287 research outputs found
Improving Phase Change Memory Performance with Data Content Aware Access
A prominent characteristic of write operation in Phase-Change Memory (PCM) is
that its latency and energy are sensitive to the data to be written as well as
the content that is overwritten. We observe that overwriting unknown memory
content can incur significantly higher latency and energy compared to
overwriting known all-zeros or all-ones content. This is because all-zeros or
all-ones content is overwritten by programming the PCM cells only in one
direction, i.e., using either SET or RESET operations, not both. In this paper,
we propose data content aware PCM writes (DATACON), a new mechanism that
reduces the latency and energy of PCM writes by redirecting these requests to
overwrite memory locations containing all-zeros or all-ones. DATACON operates
in three steps. First, it estimates how much a PCM write access would benefit
from overwriting known content (e.g., all-zeros, or all-ones) by
comprehensively considering the number of set bits in the data to be written,
and the energy-latency trade-offs for SET and RESET operations in PCM. Second,
it translates the write address to a physical address within memory that
contains the best type of content to overwrite, and records this translation in
a table for future accesses. We exploit data access locality in workloads to
minimize the address translation overhead. Third, it re-initializes unused
memory locations with known all-zeros or all-ones content in a manner that does
not interfere with regular read and write accesses. DATACON overwrites unknown
content only when it is absolutely necessary to do so. We evaluate DATACON with
workloads from state-of-the-art machine learning applications, SPEC CPU2017,
and NAS Parallel Benchmarks. Results demonstrate that DATACON significantly
improves system performance and memory system energy consumption compared to
the best of performance-oriented state-of-the-art techniques.Comment: 18 pages, 21 figures, accepted at ACM SIGPLAN International Symposium
on Memory Management (ISMM
Optimality of the genetic code with respect to protein stability and amino acid frequencies
How robust is the natural genetic code with respect to mistranslation errors?
It has long been known that the genetic code is very efficient in limiting the
effect of point mutation. A misread codon will commonly code either for the
same amino acid or for a similar one in terms of its biochemical properties, so
the structure and function of the coded protein remain relatively unaltered.
Previous studies have attempted to address this question more quantitatively,
namely by statistically estimating the fraction of randomly generated codes
that do better than the genetic code regarding its overall robustness. In this
paper, we extend these results by investigating the role of amino acid
frequencies in the optimality of the genetic code. When measuring the relative
fitness of the natural code with respect to a random code, it is indeed natural
to assume that a translation error affecting a frequent amino acid is less
favorable than that of a rare one, at equal mutation cost. We find that taking
the amino acid frequency into account accordingly decreases the fraction of
random codes that beat the natural code, making the latter comparatively even
more robust. This effect is particularly pronounced when more refined measures
of the amino acid substitution cost are used than hydrophobicity. To show this,
we devise a new cost function by evaluating with computer experiments the
change in folding free energy caused by all possible single-site mutations in a
set of known protein structures. With this cost function, we estimate that of
the order of one random code out of 100 millions is more fit than the natural
code when taking amino acid frequencies into account. The genetic code seems
therefore structured so as to minimize the consequences of translation errors
on the 3D structure and stability of proteins.Comment: 31 pages, 2 figures, postscript fil
Near-Memory Address Translation
Memory and logic integration on the same chip is becoming increasingly cost
effective, creating the opportunity to offload data-intensive functionality to
processing units placed inside memory chips. The introduction of memory-side
processing units (MPUs) into conventional systems faces virtual memory as the
first big showstopper: without efficient hardware support for address
translation MPUs have highly limited applicability. Unfortunately, conventional
translation mechanisms fall short of providing fast translations as
contemporary memories exceed the reach of TLBs, making expensive page walks
common.
In this paper, we are the first to show that the historically important
flexibility to map any virtual page to any page frame is unnecessary in today's
servers. We find that while limiting the associativity of the
virtual-to-physical mapping incurs no penalty, it can break the
translate-then-fetch serialization if combined with careful data placement in
the MPU's memory, allowing for translation and data fetch to proceed
independently and in parallel. We propose the Distributed Inverted Page Table
(DIPTA), a near-memory structure in which the smallest memory partition keeps
the translation information for its data share, ensuring that the translation
completes together with the data fetch. DIPTA completely eliminates the
performance overhead of translation, achieving speedups of up to 3.81x and
2.13x over conventional translation using 4KB and 1GB pages respectively.Comment: 15 pages, 9 figure
Integrating an agent-based wireless sensor network within an existing multi-agent condition monitoring system
The use of wireless sensor networks for condition monitoring is gaining ground across all sectors of industry, and while their use for power engineering applications has yet been limited, they represent a viable platform for next-generation substation condition monitoring systems. For engineers to fully benefit from this new approach to condition monitoring, new sensor data must be incorporated into a single integrated system. This paper proposes the integration of an agent-based wireless sensor network with an existing agent-based condition monitoring system. It demonstrates that multi-agent systems can be extended down to the sensor level while considering the reduced energy availability of low-power embedded devices. A novel agent-based approach to data translation is presented, which is demonstrated through two case studies: a lab-based temperature and vibration monitoring system, and a proposal to integrate a wireless sensor network to an existing technology demonstrator deployed in a substation in the UK
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