Programming Massively Parallel Processors: A Hands-on by Wen-mei W. Hwu, David B. Kirk

By Wen-mei W. Hwu, David B. Kirk

Publish yr note: First released January twenty second 2010

Programming hugely Parallel Processors: A Hands-on Approach indicates either scholar alike the fundamental recommendations of parallel programming and GPU structure. quite a few concepts for developing parallel courses are explored intimately.

Case stories exhibit the improvement procedure, which starts with computational considering and ends with potent and effective parallel courses. issues of functionality, floating-point layout, parallel styles, and dynamic parallelism are lined extensive.

This best-selling advisor to CUDA and GPU parallel programming has been revised with extra parallel programming examples, commonly-used libraries similar to Thrust, and motives of the most recent instruments. With those advancements, the e-book keeps its concise, intuitive, sensible technique in line with years of road-testing within the authors' personal parallel computing courses.

Updates during this new version include:
• New insurance of CUDA 5.0, enhanced functionality, more suitable improvement instruments, elevated aid, and more
• elevated assurance of similar know-how, OpenCL and new fabric on set of rules styles, GPU clusters, host programming, and knowledge parallelism
• new case reviews (on MRI reconstruction and molecular visualization) discover the newest purposes of CUDA and GPUs for medical examine and high-performance computing

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Additional resources for Programming Massively Parallel Processors: A Hands-on Approach (2nd Edition) (Applications of GPU Computing Series)

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It can also be expressed as a dual functional: TVε (u) = sup Ω u div v − χ∗ε (v) dx v → Cc1 (Γ, Rd ) , with χ∗ε (t) = ψ 1− ◦ 1 − |t| 2 for |t| < 1, else. This reduces the tendency towards piecewise constant solutions. However, as χε still grows as fast as | · |, discontinuities and consequently, the staircasing effect still appears. Such an observation can generally be made for first-order functionals penalizing the measure-valued gradient with linear growth at ◦. One approach to overcome these defects is to incorporate higher-order derivatives into the image model.

46] with ϕ = 100, T = 425. 0449. These results demonstrate the effectiveness of our method for direction diffusion, even in cases where the staircasing effect may cause unwanted artifacts. Fast Regularization of Matrix-Valued Images 33 Fig. 1. TV regularization of SO(n) data. Left-to-right, top-to-bottom: the initial estimated field for a 4-piece piecewise constant motion field, a concentric motion field, the denoised images for the piecewise constant field and the concentric motion field. Different colors mark different orientations of the initial/estimated dense field, black arrows signify the measured motion vectors, and blue arrows demonstrate the estimated field after sampling.

5. It can be seen that for a careful choice of the regularization parameter, total variation in the group elements is seen to significantly reduce rigid motion estimation errors. Furthermore, it allows us to discern the main rigidly moving parts in the sequence by producing a scale-space of rigid motions. Visualization is accomplished by projecting the embedded matrix 34 G. Rosman et al. Fig. 2. TV regularization of SO(n) data, based on the same data from Fig. 1, with a higher-order regularity term.

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