Contemporary Languages in Parallel Computing Raymond Hummel

Current Languages

Standard Languages Distributed Memory Architectures  MPI Shared Memory Architectures  OpenMP  pthreads Graphics Processing Units  CUDA  OpenCL

Use in Academia Journal articles referencing parallel languages and libraries  MPI – 863  CUDA – 539  OpenMP – 391  OpenCL – 195  Posix - 124

MPI Stands for: Message Passing Interface Pros  Extremely Scalable  Remains the dominant model for high performance computing today  Can be used to tie implementations in other languages together  Portable  Can be run in almost all OS/hardware combinations  Bindings exist for multiple languages, from Fortran to Python  Can harness a multitude of hardware setups  MPI programs can run on both distributed memory and shared memory systems

MPI Cons  Complicated Software  Requires the programmer to wrap their head around all aspects of parallel execution  Single program must handle the behavior of every process  Complicated Hardware  Building and maintaining a cluster isn’t easy  Complicated Setup  Jobs have to be run using mpirun or mpiexec  Requires mpicc to link mpi libraries for compiler

MPI

OpenMP Stands for: Open Multi-Processing Pros  Incremental Parallelization  Parallelize just that pesky triple for-loop  Portable  Does require compiler support, but all major compilers already support it  Simple Software  Include the library, add a preprocessor directive, compile with a special flag

OpenMP Cons  Limited Use-Case  Constrained to shared memory architectures  63% of survey participants from were focused on development for individual desktops and servershttp://goparallel.sourceforge.net  Scalability limited by memory architecture  Memory bandwidth is not scaling at the same rate as computation speeds

OpenMP

POSIX Threads Stands for: Portable Operating System Interface Threads Pros  Fairly Portable  Native support in UNIX operating systems  Versions exist for Windows as well  Fine Grained Control  Can control mapping of threads to processors

POSIX Threads Cons  All-or-Nothing  Can’t use software written with pthreads on systems that don’t have support for it  Major rewrite of main function required  Complicated Software  Thread management  Limited Use-Case

POSIX Threads

CUDA Stands for: Compute Unified Device Architecture Pros  Manufacturer Support  NVIDIA is actively encouraging CUDA development  Provide lots of shiny tools for developers  Low Level Hardware Access  Because Cross-Platform Portability isn’t a priority, NVIDIA can expose low-level details

CUDA Cons  Limited Use-Case  GPU computing requires massive data parallelism  Only Compatible with NVIDIA Hardware

CUDA

OpenCL Stands for: Open Compute Language Pros  Portability  Works on all major operating systems  Heterogeneous Platform  Works on CPUs, GPUs, APUs, FPGAs, coprocessors, etc…  Works with All Major Manufacturers  AMD, Intel, NVIDIA, Qualcomm, ARM, and more

OpenCL Cons  Complicated Software  Manual Everything  Special Tuning Required  Because it cannot assume anything about the hardware on which it will run, programmer has to tell it the best way to do things

Non-Standard Languages CILK OpenACC C++ AMP

CILK Language first developed by MIT Based on C, commercial improvements extend it to C++ Championed by Intel Operates on the theory that the programmer should identify parallelism, then let the run-time divide the work between processing elements Has only 5 keywords: cilk, spawn, sync, inlet, abort CILK Plus implementation merged into version 4.9 of the GNU C and C++ compilers

OpenACC Stands for: Open ACCelerators Not currently supported by major compilers Aims to function like OpenMP, but for heterogeneous CPU/GPU systems NVIDIA’s answer to OpenCL

C++ AMP Stands for: C++ Accelerated Massive Parallelism Library implemented on DirectX 11 and an open specification by Microsoft Visual Studio 2012 and up provide Debugging and Profiling support Works on any hardware that has DirectX 11 drivers

Future Languages

Developing Languages D Rust Harlan

D Performance of Compiled Languages Memory Safety Expressiveness of Dynamic Languages Includes a Concurrency Aware Type-System Nearing Maturity

Rust Designed for creation of large Client-Server Programs on the Internet Safety Memory Layout Concurrency Still Major Changes Occurring

Harlan Experimental Language Based on Scheme Designed to take care of boilerplate for GPU Programming Could be expanded to include automatic scheduling for both CPU and GPU, depending on available resources.

Questions?