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Characteristics of Dynamic JVM Languages (VMIL13)
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They use various metrics to characterize the dynamic behavior of a variety of programs written in three dynamic languages (Clojure, Python, and Ruby) executing on the JVM. | |
We have previously presented a comprehensive toolchain [20] for workload characterization across JVM languages2, which was successfully applied in studying the differences. | |
The metrics are: | |
Call-site Polymorphism. | |
Hints at opportunities for optimizations at polymorphic call-sites, e.g. inline caching [13] (based on the number of receiver types), or method inlining [10] (based on the number of target methods). => On average, 98% call site only has one targte method. | |
Field, Object, and Class Immutability. | |
Enables load elimination [3] (replacing repeated accessess to immutable objects with an access to a compiler-generated temporary stored in a register), and identifies objects and side-effectfree data structures amenable to parallelization. | |
Object Lifetimes. | |
Determines garbage-collector (GC) workload,and aids in design and evaluation of new GC algorithms, e.g. the lifetime-aware GC [15]. | |
Unnecessary Zeroing. | |
Hints at opportunities for eliminating unnecessary zeroing of memory for newly allocated objects, which comes with a performance penalty [25]. | |
Identity Hash-code Usage. | |
Hints at opportunities for reducing header size for objects that never need to store their identity hash code (often derived from their memory location upon first request [1]). | |
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Some benchmarks:
DaCapo [4], SPECjvm20084, or the Scala Benchmark suite
http://users.cecs.anu.edu.au/~steveb/research/research-areas