Massively Parallel Algorithms - SS 2016

News


About this Course

There are big changes afoot. The era of increased performance from faster single cores and optimized single core programs has ended. Instead, highly parallel GPU cores, initially developed for shading, can now run hundreds or thousands of threads in parallel. Consequently, they are increasingly being adopted to offload and augment conventional (albeit multi-core) CPUs. And the technology is getting better, faster, and cheaper. It will probably even become a general computing processor on mobile devices, because it offers more processing power per energy amount.

The high number of parallel cores, however, poses a great challenge for software and algorithm design that must expose massive parallelism to benefit from the new hardware architecture. The main purpose of the lecture is to teach practical algorithm design for such parallel hardware.

Simulation is widely regarded as the third pillar of science (in addition to experimentation and theory). Simulation has an ever increasing demand for high-performance computing. The latter has received a boost with the advent of GPUs, and it is even becoming -- to some extent -- a commodity.

There are many scientific areas where the knowledge you will gain in this course can be very valuable and useful to you:

In this course, you will get hands-on experience in developing software for massively parallel computing architectures. For the first half of the lecture, there will be supervised exercises to familiarize yourself with the CUDA parallel programming model and environment. The exercises will comprise, for instance, image processing algorithms, such as you might find in Photoshop. In the second half, you can decide whether to continue with exercises, or whether to work on a small self-assignment for the rest of the cource.

Prerequisites are:

  1. A little bit of experience with C/C++ ; note that we will need just plain old C during this course, but the concept of pointers should be familiar.
  2. Liking for algorithmic thinking.

Useful, but not required, is a computer/notebook with an Nvidia GPU that is CUDA capable. You can find a list of all supported GPU's here. If you don't have access to such a computer, you are welcome to work on your assignments in our lab.

Not required are

Slides

The following table contains the topics and the accompanying slides (it will be filled step-by-step after the respective lectures).

Week Topics Slides Assignments Frameworks
1. Introduction: More Moore, stream programming model, Flynn's taxonomy, overall speedup, Amdahl's law, Gustafson's law.
Fundamental Concepts of Parallel Programming 1: terminology, control flow, transfering data, blocks, data flow in general 		PDF1 PDF2 PDF3 Assignment 1 CUDA Introduction
2. Fundamental Concepts of Parallel Programming 2: multi-dimensional blocks and grids, classes in CUDA, constant memory, simplistic raytracer, warps, thread divergence, more details on the GPU architecture, warps, measuring performance, GPU/CPU synchronization, shared memory, parallel computation of the dot product, barrier synchronization, race conditions, document similarity PDF Assignment 2 Memcpy and Kernel Launch
3. Fundamental Concepts of Parallel Programming 3: utilizing lockstepped threads instad of explicit synchronization, heat transfer simulation, double buffering pattern, texture memory, edge detection (Sobel operator), GPU's memory hierarchy, parallel histogram computation, atomic operations, pipelining host-GPU calls PDF Assignment 3 Vector_add
4. Dense matrix algorithms: matrix vector product, column major storage, AoS versus SoA, coalesced memory access, auto-tuning, arithmetic intensity, matrix-matrix multiplication, blocked matrix multiplication, All Pairs Shortest Path as matrix multiplication, sparse matrices. PDF Assignment 4 Julia Fractals
5. Parallel prefix-sum 1: definition, simple examples, Hillis-Steele algorithm, depth & work complexity, Blelloch's algorithm, summed area tables, better percision for integral images, depth-of-field rendering (gathering & scattering), anisotropic texture filtering, face detection with Viola-Jones. PDF Assignment 5 ReduceMaxSum
6. Parallel prefix-sum 2: Brent's theorem & optimization, application to prefix-sum, theoretical speedup based on Brent, split operation, quick introduction into sequential radix sort, radix sort on the GPU, stream compaction. PDF1 Assignment 6 LineOfSight
7. Parallel sorting: comparator networks, the 0-1 principle, odd-even mergesort, bitonic sorting, work and depth complexities, (digression: adaptive bitonic sorting) PDF Assignment 7
8. Applications of parallel sorting: BVH construction, space filling curves, Morton codes; deformable collision detection by sweep plane approach, PCA transformation, and clustering; faster ray-tracing by coherent ray packets. PDF Assignment 8
9. Dynamic Parallelism: general usage, execution model, simple example, caveats, Mariani-Silver algorithm for the Mandelbrot set, the "W" pattern for multigrids.
Random Forests 1 (classification problem, simple solutions, concept of decision trees) 		PDF1 PDF2
10. Random Forests 2: information gain, entropy, conditional entropy, construction of decision trees, problems of decision trees, wisdom of crowds, bootstrapping/subsampling, randomization during construction PDF Assignment 9 Raytracer
11. Random Forests 3: variants of RF's, out-of-bag error estimation, parallel construction of RFs, handwritten digit recognition, body tracking using depth images. PDF
12./13. Of Collisions and Spheres: collision detection basics, inner sphere trees, bacht neural gas, parallel bounding volume hierarchy construction, parallel distance queries for point clouds, parallel constant time collision detection, sphere packings PDF
14. Massively parallel hash tables and their applications; intersection of point clouds, geometric hashing, massively-parallel geometric hashing, cuckoo hashing, proof for fact that cuckoo hashing works with high probability PDF

Some very simple examples that can serve as a starting point. They contain a makefile and should compile out-of-the-box at least under Max OS X and Linux.

Textbooks

The following textbooks can help review the material covered in class:

Please note that the course is not based on one single textbook! Some topics might even not be covered in any current textbook! So, I suggest you first look at the books in the library before purchasing a copy.

If you plan on buying one of these books, you might want to consider buying a used copy -- they can often be purchased for a fraction of the price of a new one. Two good internet used book shops are Abebooks and BookButler.

Grades and Points achieved by the Assignments

For taking part in a so-called "Fachgespräch" (mini oral exam), you need a grade from the assignments >= 4.0 . You can get this by successfully completing at least 40% of all asignments.

Additional Literature

Other Interesting Bits and Pieces

Gabriel Zachmann
Last modified: Thu Apr 13 11:06:48 CEST 2017