The world's fastest file manager for OS X. The world's most reliable file manager for OS X. The world's only 'Unix' file manager for OS X. Find yourself lost and frustrated with Finder? Find the alternatives too dated and too slow? Try Xfile - and take co
I am sure that all web hosts would like to lower the CPU load of their servers, shorten page load times, and boost overall performance. Whether it be to increase profit margin by packing in more customers or to get a Celeron 1.7Ghz handle a popular forum,
Whole-program optimization is a compilation technique in which optimizations operate over the entire program. This allows the compiler many optimization opportunities that are not available when analyzing modules separately (as with separate compilation). Most of MLton's optimizations are whole-program optimizations. Because MLton compiles the whole program at once, it can perform optimization across module boundaries. As a consequence, MLton often reduces or eliminates the run-time penalty that arises with separate compilation of SML features such as functors, modules, polymorphism, and higher-order functions. MLton takes advantage of having the entire program to perform transformations such as: defunctorization, monomorphisation, higher-order control-flow analysis, inlining, unboxing, argument flattening, redundant-argument removal, constant folding, and representation selection. Whole-program compilation is an integral part of the design of MLton and is not likely to change.
Top position in search results not only depends on how popular content you have but also how your website is designed.
This means that SEO and website design have an internal relation during website creation. Let’s have some points and a
brief explanation of how these two fields are interrelated.
an instrumentation framework for building dynamic analysis tools. Includes a memory error detector, two thread error detectors, a cache and branch-prediction profiler, a call-graph generating cache profiler, and a heap profiler. It also includes two experimental tools: a heap/stack/global array overrun detector, and a SimPoint basic block vector generator.
Data analytics is becoming increasingly prominent in a variety
of application areas ranging from extracting business intelligence
to processing data from scientific studies. MapReduce
programming paradigm lends itself well to these data-intensive
analytics jobs, given its ability to scale-out and leverage several
machines to parallely process data. In this work we argue
that such MapReduce-based analytics are particularly synergistic
with the pay-as-you-go model of a cloud platform. However,
a key challenge facing end-users in this environment is
the ability to provision MapReduce applications to minimize
the incurred cost, while obtaining the best performance. This
paper firstmotivates the importance of optimally provisioning a
MapReduce job, and demonstrates that existing approaches can
result in far from optimal provisioning. We then present a preliminary
approach that improves MapReduce provisioning by
analyzing and comparing resource consumption of the application
at hand with a database of similar resource consumption
signatures of other applications.
The Cloudera Solutions team shares their insights into getting the most out of your Hadoop deployment. Webinar recording available on www.cloudera.com/events