Server and workstation hardware architecture is continually improving, yet interpreted languages have failed to keep pace with the proper utilization of modern processors. SIMD (single instruction, multiple data) units are available in nearly every current desktop and server processor and are greatly underutilized, especially with interpreted languages. If multicore processors continue their current growth pattern, interpreted-language performance will begin to fall behind, since current native compilers and languages offer better automated SIMD optimization and direct SIMD mapping support. As each core in commercial x86 multicore processors includes a dedicated SIMD unit, the performance disparity will grow exponentially as long as the available SIMD units remain underutilized in interpreted-language environments.
[1]
John Shalf,et al.
SEJITS: Getting Productivity and Performance With Selective Embedded JIT Specialization
,
2010
.
[2]
Omar Hammami,et al.
Application-specific SIMD synthesis for reconfigurable architectures
,
2006,
Microprocess. Microsystems.
[3]
Miriam Leeser,et al.
Multimedia Macros for Portable Optimized Programs
,
2004
.
[4]
Voicu Groza,et al.
Design of a custom vector operation API exploiting SIMD intrinsics within Java
,
2010,
CCECE 2010.
[5]
Web Guru.
Adobe and NVIDIA announce GPU acceleration for Flash Player
,
2009
.
[6]
Denis Caromel,et al.
Current State of Java for HPC
,
2008
.