Primitive data type

Summary

In computer science, primitive data types are a set of basic data types from which all other data types are constructed. Specifically it often refers to the limited set of data representations in use by a particular processor, which all compiled programs must use. Most processors support a similar set of primitive data types, although the specific representations vary. More generally, "primitive data types" may refer to the standard data types built into a programming language (built-in types). Data types which are not primitive are referred to as derived or composite. Primitive types are almost always value types, but composite types may also be value types. Common primitive data types The most common primitive types are those used and supported by computer hardware, such as integers of various sizes, floating-point numbers, and Boolean logical values. Operations on such types are usually quite efficient. Primitive data types which are native to the pro

Official source

https://en.wikipedia.org/wiki/Primitive_data_type

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Miniboxing: An Encoding for Specialization

Cristian Talau

In the presence of parametric polymorphism, erasure-based languages such as Java and Scala handle primitives (boolean values, integers and floating point numbers) in a suboptimal way: in order to provide a uniform representation on the low level, all primitive values are stored in heap objects, in a process known as boxing. This leads to access overheads, wasteful usage of the heap space and broken cache locality. Specialization enables Scala to optimally handle primitive values in the context of generic classes, but this is done at the expense of duplicating classes up to 10 times for each type parameter, for each of the 9 Scala primitive types and heap objects. This prevents specialization of key classes in the Scala library, such as Function2, List, Map and so on. This project aims at testing the hypothesis that encoding several primitive types into a larger stack-based primitive type can maintain the performance of specialized code, while dramatically decreasing the generated bytecode size.

2012

Compiling Scala for Performance

Iulian Dragos

Scala is a new programming language bringing together object-oriented and functional programming. Its defining features are uniformity and extensibility. Scala offers great flexibility for programmers, allowing them to grow the language through libraries. Oftentimes what seems like a language feature is in fact implemented in a library, effectively giving programmers the power of language designers. The downside of this flexibility is that familiar looking code may hide unexpected performance costs. It is important for Scala compilers to bring down this cost as much as possible. We identify several areas of impact for Scala performance: higher-order functions and closures, and generic containers used with primitive types. We present two complementary approaches for improving performance in these areas: optimizations and specialization. Compiler optimization can bring down the cost through a combination of aggressive inlining of higher-order functions, an extended version of copy-propagation and dead-code elimination. Both anonymous functions and boxing can be eliminated by this approach. We show on a number of benchmarks that these language features can be up to 5 times faster when properly optimized, on current day JVMs. We propose a new approach to compiling parametric polymorphism for performance at primitive types. We mix a homogeneous translation scheme with user-directed specialization for primitive types. Type parameters may be annotated to require specialization of code depending on them. We propose definition-site specialization for primitive types, achieving separate compilation and no boxing when both the definition and call site are specialized. Specialized classes are compatible with unspecialized code, and specialization agnostic code can work with specialized instances, meaning that specialization is opportunistic. We present a formalism of a small subset of Scala with specialization and prove that specialization preserves types. We implemented this translation in the Scala compiler and report on improvements on a set of benchmarks, showing that specialization can make programs more than two times faster.

EPFL2010

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Generics on the Java platform are compiled using the erasure transformation, which only supports by-reference values. This causes slowdowns when generics operate on primitive types, such as integers, as they have to be transformed into reference-based objects. Project Valhalla is an effort to remedy this problem by specializing classes at load-time so they can efficiently handle primitive values. In its current early prototype, the Valhalla compilation scheme limits the interaction between specialized and erased generics, thus preventing certain useful code patterns from being expressed. Scala has been using compile-time specialization for 6 years and has three generics compilation schemes working side by side. In Scala, programmers are allowed to write code that freely exercises the interaction between the different compilation schemes, at the expense of introducing subtle performance issues. Similar performance issues can affect Valhalla-enabled bytecode, whether the code was written in Java or translated from other JVM languages. In this context we explain how we help programmers avoid these performance regressions in the miniboxing transformation: (1) by issuing actionable performance advisories that steer programmers away from performance regressions and (2) by providing alternatives to the standard library constructs that use the miniboxing encoding, thus avoiding the conversion overhead.

ACM2015

Compiling Scala for Performance

Iulian Dragos

EPFL2010