4 research outputs found
PartRePer-MPI: Combining Fault Tolerance and Performance for MPI Applications
As we have entered Exascale computing, the faults in high-performance systems
are expected to increase considerably. To compensate for a higher failure rate,
the standard checkpoint/restart technique would need to create checkpoints at a
much higher frequency resulting in an excessive amount of overhead which would
not be sustainable for many scientific applications. Replication allows for
fast recovery from failures by simply dropping the failed processes and using
their replicas to continue the regular operation of the application.
In this paper, we have implemented PartRePer-MPI, a novel fault-tolerant MPI
library that adopts partial replication of some of the launched MPI processes
in order to provide resilience from failures. The novelty of our work is that
it combines both fault tolerance, due to the use of the User Level Failure
Mitigation (ULFM) framework in the Open MPI library, and high performance, due
to the use of communication protocols in the native MPI library that is
generally fine-tuned for specific HPC platforms. We have implemented efficient
and parallel communication strategies with computational and replica processes,
and our library can seamlessly provide fault tolerance support to an existing
MPI application. Our experiments using seven NAS Parallel Benchmarks and two
scientific applications show that the failure-free overheads in PartRePer-MPI
when compared to the baseline MVAPICH2, are only up to 6.4% for the NAS
parallel benchmarks and up to 9.7% for the scientific applications
Noise removal methods on ambulatory EEG: A Survey
Over many decades, research is being attempted for the removal of noise in
the ambulatory EEG. In this respect, an enormous number of research papers is
published for identification of noise removal, It is difficult to present a
detailed review of all these literature. Therefore, in this paper, an attempt
has been made to review the detection and removal of an noise. More than 100
research papers have been discussed to discern the techniques for detecting and
removal the ambulatory EEG. Further, the literature survey shows that the
pattern recognition required to detect ambulatory method, eye open and close,
varies with different conditions of EEG datasets. This is mainly due to the
fact that EEG detected under different conditions has different
characteristics. This is, in turn, necessitates the identification of pattern
recognition technique to effectively distinguish EEG noise data from a various
condition of EEG data
Queer In AI: A Case Study in Community-Led Participatory AI
We present Queer in AI as a case study for community-led participatory design
in AI. We examine how participatory design and intersectional tenets started
and shaped this community's programs over the years. We discuss different
challenges that emerged in the process, look at ways this organization has
fallen short of operationalizing participatory and intersectional principles,
and then assess the organization's impact. Queer in AI provides important
lessons and insights for practitioners and theorists of participatory methods
broadly through its rejection of hierarchy in favor of decentralization,
success at building aid and programs by and for the queer community, and effort
to change actors and institutions outside of the queer community. Finally, we
theorize how communities like Queer in AI contribute to the participatory
design in AI more broadly by fostering cultures of participation in AI,
welcoming and empowering marginalized participants, critiquing poor or
exploitative participatory practices, and bringing participation to
institutions outside of individual research projects. Queer in AI's work serves
as a case study of grassroots activism and participatory methods within AI,
demonstrating the potential of community-led participatory methods and
intersectional praxis, while also providing challenges, case studies, and
nuanced insights to researchers developing and using participatory methods.Comment: To appear at FAccT 202