How not to lie with statistics: the correct way to summarize benchmark results
Communications of the ACM - The MIT Press scientific computation series
An efficient implementation of SELF a dynamically-typed object-oriented language based on prototypes
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Design patterns: elements of reusable object-oriented software
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Dynamo: a transparent dynamic optimization system
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Statistically rigorous java performance evaluation
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No bit left behind: the limits of heap data compression
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Trace-based just-in-time type specialization for dynamic languages
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Tracing the meta-level: PyPy's tracing JIT compiler
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An analysis of the dynamic behavior of JavaScript programs
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Evaluating the dynamic behaviour of Python applications
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Allocation removal by partial evaluation in a tracing JIT
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Dynamically typed language implementations often use more memory and execute slower than their statically typed cousins, in part because operations on collections of elements are unoptimised. This paper describes storage strategies, which dynamically optimise collections whose elements are instances of the same primitive type. We implement storage strategies in the PyPy virtual machine, giving a performance increase of 18% on wide-ranging benchmarks of real Python programs. We show that storage strategies are simple to implement, needing only 1500LoC in PyPy, and have applicability to a wide range of virtual machines.