5,140 research outputs found
Towards co-designed optimizations in parallel frameworks: A MapReduce case study
The explosion of Big Data was followed by the proliferation of numerous
complex parallel software stacks whose aim is to tackle the challenges of data
deluge. A drawback of a such multi-layered hierarchical deployment is the
inability to maintain and delegate vital semantic information between layers in
the stack. Software abstractions increase the semantic distance between an
application and its generated code. However, parallel software frameworks
contain inherent semantic information that general purpose compilers are not
designed to exploit.
This paper presents a case study demonstrating how the specific semantic
information of the MapReduce paradigm can be exploited on multicore
architectures. MR4J has been implemented in Java and evaluated against
hand-optimized C and C++ equivalents. The initial observed results led to the
design of a semantically aware optimizer that runs automatically without
requiring modification to application code.
The optimizer is able to speedup the execution time of MR4J by up to 2.0x.
The introduced optimization not only improves the performance of the generated
code, during the map phase, but also reduces the pressure on the garbage
collector. This demonstrates how semantic information can be harnessed without
sacrificing sound software engineering practices when using parallel software
frameworks.Comment: 8 page
MapReduce is Good Enough? If All You Have is a Hammer, Throw Away Everything That's Not a Nail!
Hadoop is currently the large-scale data analysis "hammer" of choice, but
there exist classes of algorithms that aren't "nails", in the sense that they
are not particularly amenable to the MapReduce programming model. To address
this, researchers have proposed MapReduce extensions or alternative programming
models in which these algorithms can be elegantly expressed. This essay
espouses a very different position: that MapReduce is "good enough", and that
instead of trying to invent screwdrivers, we should simply get rid of
everything that's not a nail. To be more specific, much discussion in the
literature surrounds the fact that iterative algorithms are a poor fit for
MapReduce: the simple solution is to find alternative non-iterative algorithms
that solve the same problem. This essay captures my personal experiences as an
academic researcher as well as a software engineer in a "real-world" production
analytics environment. From this combined perspective I reflect on the current
state and future of "big data" research
Experimental Performance Evaluation of Cloud-Based Analytics-as-a-Service
An increasing number of Analytics-as-a-Service solutions has recently seen
the light, in the landscape of cloud-based services. These services allow
flexible composition of compute and storage components, that create powerful
data ingestion and processing pipelines. This work is a first attempt at an
experimental evaluation of analytic application performance executed using a
wide range of storage service configurations. We present an intuitive notion of
data locality, that we use as a proxy to rank different service compositions in
terms of expected performance. Through an empirical analysis, we dissect the
performance achieved by analytic workloads and unveil problems due to the
impedance mismatch that arise in some configurations. Our work paves the way to
a better understanding of modern cloud-based analytic services and their
performance, both for its end-users and their providers.Comment: Longer version of the paper in Submission at IEEE CLOUD'1
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