6,825 research outputs found
FlexOS: Towards Flexible OS Isolation
At design time, modern operating systems are locked in a specific safety and
isolation strategy that mixes one or more hardware/software protection
mechanisms (e.g. user/kernel separation); revisiting these choices after
deployment requires a major refactoring effort. This rigid approach shows its
limits given the wide variety of modern applications' safety/performance
requirements, when new hardware isolation mechanisms are rolled out, or when
existing ones break.
We present FlexOS, a novel OS allowing users to easily specialize the safety
and isolation strategy of an OS at compilation/deployment time instead of
design time. This modular LibOS is composed of fine-grained components that can
be isolated via a range of hardware protection mechanisms with various data
sharing strategies and additional software hardening. The OS ships with an
exploration technique helping the user navigate the vast safety/performance
design space it unlocks. We implement a prototype of the system and
demonstrate, for several applications (Redis/Nginx/SQLite), FlexOS' vast
configuration space as well as the efficiency of the exploration technique: we
evaluate 80 FlexOS configurations for Redis and show how that space can be
probabilistically subset to the 5 safest ones under a given performance budget.
We also show that, under equivalent configurations, FlexOS performs similarly
or better than several baselines/competitors.Comment: Artifact Evaluation Repository:
https://github.com/project-flexos/asplos22-a
Analyzing the Reliability of Alternative Convolution Implementations for Deep Learning Applications
Convolution represents the core of Deep Learning (DL) applications, enabling the automatic extraction of features from raw input data. Several implementations of the convolution have been proposed. The impact of these different implementations on the performance of DL applications has been studied. However, no specific reliability-related analysis has been carried out. In this paper, we apply the CLASSES cross-layer reliability analysis methodology for an in-depth study aimed at: i) analyzing and characterizing the effects of Single Event Upsets occurring in Graphics Processing Units while executing the convolution operators; and ii) identifying whether a convolution implementation is more robust than others. The outcomes can then be exploited to tailor better hardening schemes for DL applications to improve reliability and reduce overhead
Massive MIMO for Internet of Things (IoT) Connectivity
Massive MIMO is considered to be one of the key technologies in the emerging
5G systems, but also a concept applicable to other wireless systems. Exploiting
the large number of degrees of freedom (DoFs) of massive MIMO essential for
achieving high spectral efficiency, high data rates and extreme spatial
multiplexing of densely distributed users. On the one hand, the benefits of
applying massive MIMO for broadband communication are well known and there has
been a large body of research on designing communication schemes to support
high rates. On the other hand, using massive MIMO for Internet-of-Things (IoT)
is still a developing topic, as IoT connectivity has requirements and
constraints that are significantly different from the broadband connections. In
this paper we investigate the applicability of massive MIMO to IoT
connectivity. Specifically, we treat the two generic types of IoT connections
envisioned in 5G: massive machine-type communication (mMTC) and ultra-reliable
low-latency communication (URLLC). This paper fills this important gap by
identifying the opportunities and challenges in exploiting massive MIMO for IoT
connectivity. We provide insights into the trade-offs that emerge when massive
MIMO is applied to mMTC or URLLC and present a number of suitable communication
schemes. The discussion continues to the questions of network slicing of the
wireless resources and the use of massive MIMO to simultaneously support IoT
connections with very heterogeneous requirements. The main conclusion is that
massive MIMO can bring benefits to the scenarios with IoT connectivity, but it
requires tight integration of the physical-layer techniques with the protocol
design.Comment: Submitted for publicatio
New Techniques for On-line Testing and Fault Mitigation in GPUs
L'abstract è presente nell'allegato / the abstract is in the attachmen
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