This paper reports on CuPP, our newly developed C++ framework designed to ease integration of NVIDIA’s GPGPU system, CUDA, into existing C++ applications. CuPP provides interfaces to reoccurring tasks that are easier to use than the standard CUDA interfaces. In this paper we concentrate on memory management and related data structures. CuPP offers both a low level interface — mostly consisting of smart pointers and memory allocation functions for GPU memory — and a high level interface offering a C++ STL vector wrapper and the so-called type transformations. The wrapper can be used by both device and host to automatically keep data in sync. The type transformations allow developers to write their own data structures offering the same functionality as the CuPP vector, in case a vector does not conform to the need of the application. Furthermore the type transformations offer a way to have two different representations for the same data at host and device, respectively. We demonstrate the benefits of using CuPP by integrating it into an example application, the open-source steering library OpenSteer. In particular, for this application we develop a uniform grid data structure to solve the k-nearest neighbor problem that deploys the type transformations. The paper finishes with a brief outline of another CUDA application, the Einstein@Home client, which also requires data structure redesign and thus may benefit from the type transformations and future work on CuPP.

(Jens Breitbart:  CuPP – A framework for easy CUDA integration, HiPS 2009 workshop with IPDPS 2009, Rome, Italy, May 2009)

(Massimiliano Fatica, Timo Stich, and Graham Pullan.  High Performance Computing with CUDA.  Tutorial.  International Conference on Supercomputing 2009.  Hamburg, Germany.)

Abstract:

Scan and segmented scan algorithms are crucial building blocks for a great many data-parallel algorithms. Segmented scan and related primitives also provide the necessary support for the flattening transform, which allows for nested data-parallel programs to be compiled into flat data-parallel languages. In this paper, we describe the design of efficient scan and segmented scan parallel primitives in CUDA for execution on GPUs. Our algorithms are designed using a divide-and-conquer approach that builds all scan primitives on top of a set of primitive intra-warp scan routines. We demonstrate that this design methodology results in routines that are simple, highly efficient, and free of irregular access patterns that lead to memory bank conflicts. These algorithms form the basis for current and upcoming releases of the widely used CUDPP library.

(S. Sengupta, M. Harris, and M. Garland. Efficient parallel scan algorithms for GPUs. NVIDIA Technical Report NVR-2008-003, December 2008)

From the press release:

The Portland Group®, a wholly-owned subsidiary of STMicroelectronics and leading supplier of compilers for high-performance computing (HPC), today announced an agreement with NVIDIA under which the two companies plan to develop new Fortran language support for CUDA GPUs.

The NVIDIA® CUDA™ architecture allows developers to offload computationally intensive kernels to the massively parallel GPU. Through function calls and language extensions, CUDA gives developers explicit control over the mapping of general-purpose computational kernels to GPUs as well as placement and movement of data between the x64 processor and the GPU. The NVIDIA CUDA C compiler already provides this capability to C programmers. The CUDA Fortran compiler will provide this same level of control and optimization in a native Fortran environment from PGI.

From the press release:

The use of Graphics Processing Units (GPUs) as general purpose accelerators has been a growing trend in high-performance computing (HPC). Until now, use of GPUs from Fortran applications has been extremely limited. Developers targeting GPU accelerators have had to program in C at a detailed level using sequences of function calls to manage movement of data between the x64 host and GPU, and to offload computations from the host to the GPU. The PGI Accelerator Fortran and C compilers automatically analyze whole program structure and data, split portions of an application between a multi-core x64 CPU and a GPU as specified by user directives, and define and generate a mapping of loops to automatically use the parallel cores, hardware threading capabilities and SIMD vector capabilities of modern GPUs.

The PGI Accelerator programming model is available now in the PGI Fortran and C compilers on Linux as an extended free preview for all PGI 9.0 licensees through the end of 2009. Additional PGI 9.0 new features include several Fortran 2003 incremental features, Intel Xeon EX (Nehalem) optimizations including support for SSE 4.1/4.2, Six-Core AMD Opteron (Istanbul) support and optimizations, full OpenMP 3.0 support in C++, an all-new graphical interface on the PGDBG OpenMP/MPI debugger, enhancements to the PGPROF performance profiling environment, and support for Fedora Core 10/11, SuSE 11.1 and Ubuntu 9.

This paper was presented in the Workshop on Architecture-Aware Simulation and Computing, organized by Michael Bader and Josef Weidendorfer (Technische Universität München). Three other GPGPU papers were part of this workshop:

• GPU Acceleration of an Unmodified Parallel Finite Element Navier–Stokes Solver by Dominik Göddeke, Sven H.M. Buijssen, Hilmar Wobker and Stefan Turek. This contribution also received a Best Paper Award nomination.
• Comparing CUDA and OpenGL Implementations for a Jacobi Iteration by Ronan Amorim, Gundolf Haase, Manfred Liebmann and Rodrigo Weber dos Santos
• Data Structure Design for GPU Based Heterogeneous Systems by Jens Breitbart

The abstract of the award-winning paper is:

We have designed a fast parallel simulator that solves the acoustic wave equation on a GPU cluster. Solving the acoustic wave equation in an oil exploration industrial context aims at speeding up seismic modeling and Reverse Time Migration. We consider a finite difference approach on a regular mesh, in both 2D and 3D cases. The acoustic wave equation is solved in either a constant density or a variable density domain. All the computations are done in single precision, since double precision is not required in our context. We use CUDA to take advantage of the GPUs computational power. We study different implementations and their impact on the application performance. We obtain a speed up of 10 for Reverse Time Migration and up to 30 for the modeling application over a code running on general purpose CPUs.

Submissions should be mailed to gecco2009@gpgpgpu.com no later than June 23, 2009. The final scores will be announced during GECCO. More information is available at the following sites.

http://www.gpgpgpu.com/gecco2009/
http://www.sigevo.org/gecco-2009/competitions.html

Entrants must submit (1) the application source code with instructions to compile it and (2) a two-page description of the application. Submissions will be reviewed by a committee of researchers from the evolutionary computation community and from industry. Each reviewer will score the submission according to 12 criteria concerning the submitted algorithm, the speed-up it achieves, and its impact on the evolutionary computation community. The total score will be obtained as the weighted sum of the 12 separate scores.