1,707 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
An Approach to Ad hoc Cloud Computing
We consider how underused computing resources within an enterprise may be
harnessed to improve utilization and create an elastic computing
infrastructure. Most current cloud provision involves a data center model, in
which clusters of machines are dedicated to running cloud infrastructure
software. We propose an additional model, the ad hoc cloud, in which
infrastructure software is distributed over resources harvested from machines
already in existence within an enterprise. In contrast to the data center cloud
model, resource levels are not established a priori, nor are resources
dedicated exclusively to the cloud while in use. A participating machine is not
dedicated to the cloud, but has some other primary purpose such as running
interactive processes for a particular user. We outline the major
implementation challenges and one approach to tackling them
funcX: A Federated Function Serving Fabric for Science
Exploding data volumes and velocities, new computational methods and
platforms, and ubiquitous connectivity demand new approaches to computation in
the sciences. These new approaches must enable computation to be mobile, so
that, for example, it can occur near data, be triggered by events (e.g.,
arrival of new data), be offloaded to specialized accelerators, or run remotely
where resources are available. They also require new design approaches in which
monolithic applications can be decomposed into smaller components, that may in
turn be executed separately and on the most suitable resources. To address
these needs we present funcX---a distributed function as a service (FaaS)
platform that enables flexible, scalable, and high performance remote function
execution. funcX's endpoint software can transform existing clouds, clusters,
and supercomputers into function serving systems, while funcX's cloud-hosted
service provides transparent, secure, and reliable function execution across a
federated ecosystem of endpoints. We motivate the need for funcX with several
scientific case studies, present our prototype design and implementation, show
optimizations that deliver throughput in excess of 1 million functions per
second, and demonstrate, via experiments on two supercomputers, that funcX can
scale to more than more than 130000 concurrent workers.Comment: Accepted to ACM Symposium on High-Performance Parallel and
Distributed Computing (HPDC 2020). arXiv admin note: substantial text overlap
with arXiv:1908.0490
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