Advanced Software Technologies for Post-Peta Scale Computing by Mitsuhisa Sato

Author:Mitsuhisa Sato
Language: eng
Format: epub
ISBN: 9789811319242
Publisher: Springer Singapore

Then line 5 and 6 in Listing 8.3 can be replaced with the following single call:

 mapmap(f, pos1, pos2);

Defining a convenience method such as mapmap is not possible if map is a macro function.

8.3 Bytespresso

To show our idea of deep reification, we implemented a Java library that provides deep reification in Java [2]. Our Java library named Bytespresso extracts an AST by bytecode decompilation. It needs to launch the Java virtual machine with the option jdk.internal.lambda.dumpProxyClasses. This option generates the bytecode of a dynamically generated lambda expression. For deep reification, Bytespresso reads the bytecode of a given lambda expression and decompiles it to construct an AST. It does not need source code; it only needs Java bytecode.

To support the implementation of an embedded DSL by deep reification, Bytespresso also provides a translator from ASTs to C or CUDA code. The code generated by the translation is normally compiled by an external C (or CUDA) compiler and executed in a separate process from the Java virtual machine. The generated code and the host Java code communicate with each other through a socket for portability.

Delimiting AST acquisition by deep reification, Bytespresso provides the @Native annotation. When a method has this annotation, the AST of the method body is not extracted by deep reification. The ASTs for the methods invoked by that method are not extracted either. Although a @Native method is translated into a C function by Bytespresso, the body of that C function is the argument to @Native. The following method is an example of @Native method:

 @Native( "struct timeval time; gettimeofday(&time, NULL); "

  + "return time.tv_sec * 1000000 + time.tv_usec;")

 public static long time() {

  return System.nanoTime() / 1000;

 }

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