clpp is an OpenCL library of data-parallel algorithm primitives such as parallel prefix sum (“scan”), parallel sort and parallel reduction. Primitives such as these are important building blocks for a wide variety of data-parallel algorithms, including sorting, stream compaction, and building data structures such as trees and summed-area tables. For more information, visit http://code.google.com/p/clpp.

Today NVIDIA announced the upcoming 4.0 release of CUDA.  While most of the major CUDA releases accompanied a new GPU architecture, 4.0 is a software-only release, but that doesn’t mean there aren’t a lot of new features.  With this release, NVIDIA is aiming to lower the barrier to entry to parallel programming on GPUs, with new features including easier multi-GPU programming, a unified virtual memory address space, the powerful Thrust C++ template library, and automatic performance analysis in the Visual Profiler tool.  Full details follow in the quoted press release below.

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Thrust v1.3, an open-source template library for CUDA applications, has been released. Modeled after the C++ Standard Template Library (STL), Thrust brings a familiar abstraction layer to the realm of GPU computing.

Version 1.3 adds several new features, including:

• a state-of-the-art sorting implementation, recently featured on Slashdot.
• performance improvements to stream compaction and reduction
• robust error reporting and failure detection
• support for CUDA 3.2 and gf104-based GPUs
• search algorithms
• and more!

Get started with Thrust today! First download Thrust v1.3 and then follow the online quick-start guide. Refer to the online documentation for a complete list of features. Many concrete examples and a set of introductory slides are also available. Read the rest of this entry »

Abstract:

We have previously suggested mixed precision iterative solvers specifically tailored to the iterative solution of sparse linear equation systems as they typically arise in the finite element discretization of partial differential equations. These schemes have been evaluated for a number of hardware platforms, in particular single precision GPUs as accelerators to the general purpose CPU. This paper reevaluates the situation with new mixed precision solvers that run entirely on the GPU: We demonstrate that mixed precision schemes constitute a significant performance gain over native double precision. Moreover, we present a new implementation of cyclic reduction for the parallel solution of tridiagonal systems and employ this scheme as a line relaxation smoother in our GPU-based multigrid solver. With an alternating direction implicit variant of this advanced smoother we can extend the applicability of the GPU multigrid solvers to very ill-conditioned systems arising from the discretization on anisotropic meshes, that previously had to be solved on the CPU. The resulting mixed precision schemes are always faster than double precision alone, and outperform tuned CPU solvers consistently by almost an order of magnitude.

(Dominik Göddeke and Robert Strzodka: “Cyclic Reduction Tridiagonal Solvers on GPUs Applied to Mixed Precision Multigrid” , accepted in: IEEE Transactions on Parallel and Distributed Systems, Special Issue: High Performance Computing with Accelerators, Mar. 2010. Link.)

The developers of the CUDPP (CUDA Data-Parallel Primitives) Library request that users (past and current) of the CUDPP Library fill out the CUDPP Survey.  This survey will help the CUDPP Team prioritize new development and support for existing and new features.

Thrust (v1.1) is  an open-source template library for developing CUDA applications.  Modeled after the C++ Standard Template Library (STL), Thrust brings a familiar abstraction layer to the realm of GPU computing. Version 1.1 adds several new features, including:

• fancy iterators
• binary search algorithms
• pair and tuple types
• segmented scan (experimental)
• pinned memory support (experimental)
• and more!

To get started with Thrust, first download Thrust and then follow the online tutorial.  Refer to the online documentation for a complete list of features.  Many concrete examples and a set of introductory slides are also available. As the following code example shows, Thrust programs are concise and readable. Read the rest of this entry »

This NVIDIA technical report by Sengupta, Harris, and Garland describes the design of new parallel algorithms for scan and segmented scan on GPUs.   This paper describes the primitives included in the latest release of the CUDPP library.

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)

Abstract from the paper by Rehman et al.:

General purpose programming on graphics processing units (GPGPU) has received a lot of attention in the parallel computing community as it promises to offer the highest performance per dollar. While GPUs are usually used to tackle regular problems that can be easily parallelized, we describe two implementations of List Ranking—a traditional irregular algorithm that is difficult to parallelize on such massively multi-threaded hardware. In our best implementation, we introduce a GPU-optimized, recursive version of the Helman-JaJa algorithm. Our implementation can rank a random list of 8 million elements in just over 100 milliseconds, and achieves a speedup of about 8-9 over a CPU implementation as well as a speedup of 3-4 over the best reported implementation on the Cell Broadband Engine. We also discuss some practical issues that come to the fore when working with massively multi-threaded architectures, especially for algorithms with highly irregular memory access patterns. (M. Suhail Rehman, K. Kothapalli, P.J. Narayanan. Fast and Scalable List Ranking on the GPU. 23rd International Conference on Supercomputing (ICS). New York, USA, June 2009. (To Appear))

This IPDPS 2009 paper by Nadathur Satish, Mark Harris, and Michael Garland describes the design of high-performance parallel radix sort and merge sort routines for manycore GPUs, taking advantage of the full programmability offered by NVIDIA CUDA. The radix sort described is the fastest GPU sort and the merge sort described is the fastest comparison-based GPU sort reported in the literature. The radix sort is up to 4 times faster than the graphics-based GPUSort and greater than 2 times faster than other CUDA-based radix sorts. It is also 23% faster, on average, than even a very carefully optimized multicore CPU sorting routine. To achieve this performance, the authors carefully design the algorithms to expose substantial fine-grained parallelism and decompose the computation into independent tasks that perform minimal global communication. They exploit the high-speed on-chip shared memory provided by NVIDIA’s GPU architecture and efficient data-parallel primitives, particularly parallel scan. While targeted at GPUs, these algorithms should also be well-suited for other manycore processors. (N. Satish, M. Harris, and M. Garland. Designing efficient sorting algorithms for manycore GPUs. Proc. 23rd IEEE Int’l Parallel & Distributed Processing Symposium, May 2009. To appear.)

This Ph.D. dissertation by Aaron Lefohn at the University of California, Davis describes the Glift GPU data structure abstraction and its application to both GPU-based data-parallel and interactive rendering algorithms. The applications include octree 3D painting, adaptive shadow maps, resolution matched shadow maps, heat-diffusion depth-of-field, and a GPU-based direct solver for tridiagonal linear systems. While much of this work has been posted previously, this dissertation contains a more in-depth discussion of the Glift data structure library and introduces several GPGPU and rendering algorithms that are not yet published. This dissertation demonstrates that a data structure abstraction for GPUs can simplify the description of new and existing data structures, stimulate development of complex GPU algorithms, and perform equivalently to hand-coded implementations. The dissertation also presents a case that future interactive rendering solutions will be an inseparable mix of general-purpose, data-parallel algorithms and traditional graphics programming. (Aaron Lefohn, “Glift: Generic Data Structures for Graphics Hardware”, Ph.D. dissertation, Computer Science Department, University of California Davis, September 2006.)