PARALLELIZATION OF ATTILA SIMULATOR WITH OPENMP MIGUEL ÁNGEL MARTÍNEZ DEL AMOR MINIPROJECT OF TDT24 NTNU

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1 PARALLELIZATION OF ATTILA SIMULATOR WITH OPENMP MIGUEL ÁNGEL MARTÍNEZ DEL AMOR MINIPROJECT OF TDT24 NTNU

2 OUTLINE INEFFICIENCY OF ATTILA WAYS TO PARALLELIZE LOW COMPATIBILITY IN THE COMPILATION A SOLUTION MADE WITH THE PTHREAD LIBRARY Scientific Computing 2

3 INEFFIENCY OF ATTILA (I) The simulator takes a lot of time to simulate frames: The CPU has to do a work that is very inefficient if it is not used specific hardware like GPU. This program is a cycle-accurate simulator, where everything is simulated in detail. The simulator code uses sequential model, with boxes and a lot of useless signals between them. Scientific Computing 3

4 INEFFIENCY OF ATTILA (II) Scientific Computing 4

5 INEFFIENCY OF ATTILA (III) We can see the way of working in the main loop (called from the main function, file bgpu.c): Sequential: First simulate one cycle in one box, and then simulate the next. Each box has input signals and output signals. One signal is a class that implements a kind of buffer with a cycle of delay. Scientific Computing 5

6 INEFFIENCY OF ATTILA (III) Each stage (or box) is executed for one cycle, in a sequential order Scientific Computing 6

7 WAYS TO PARALLELIZE (I) Problem 1: Statistics show that the most time of the simulation is used on the Shaders (fragment shaders and their texture units). Also ZStencilTest takes so much time when doing depth or stencil passes (like stencil shadows engines as Doom3). Scientific Computing 7

8 WAYS TO PARALLELIZE (II) Solution: Use several Fragment Shader units. Also can be interesting use more than one Vertex Shader unit and ZStencilTest unit. With the sequential implementation, the problem will be the same (each unit in execution in order). So, here, it can be used parallelization. The parallelization of the Fragment Shaders can be easy if we put a barrier for waiting before going to the next stage. Each Fragment Shader has an input and an output private signal that only the boxes connected with them can read, that is, there is not interaction between the Fragment Shader Units. Scientific Computing 8

9 WAYS TO PARALLELIZE (III) Each iteration is done by one thread, with a implicit barrier at the end Scientific Computing 9

10 WAYS TO PARALLELIZE (IV) Problem 2: In each cycle, each stage has to wait to the previous for being executed (and just one cycle), when in a real GPU it does not happens (each stage executes one cycle at the same time). The extensible model that ATTILA uses allows us to parallelize the execution of each stage in a easy way. Scientific Computing 10

11 WAYS TO PARALLELIZE (V) Solution: Scientific Computing 11

12 WAYS TO PARALLELIZE (VI) Scientific Computing 12

13 WAYS TO PARALLELIZE (VII) This solution is not easy. We have to implement a lock for the signals (buffers), statistics dumps, and not go to panic mode when there is no data in a signal. Scientific Computing 13

14 LOW COMPATIBILITY IN THE COMPILATION (I) There are three versions of gllib.a, for Linux, Cygwin and Visual Studio In Linux, this library was made by the gcc 3.2 compiler. The problem is that the library gllib.a can be linked only by the compiler that was used for that (for example, it is not possible to link with a new gcc 4.2) and the code is private. Scientific Computing 14

15 LOW COMPATIBILITY IN THE COMPILATION (II) The ways to compile an OpenMP code are to use the Intel compiler, a free compiler like OdinMP or the new gcc 4.2 version. None of them can compile/link the whole ATTILA s source code (they are not compatible with the gcc 3.2 (both Linux and Cygwin) neither with the gcc4.2). So, it is not possible to compile the simulator with a code that uses OpenMP. The authors are working in a new version of the gllib.a for the gcc 4.2 (or later), then, when this version will be ready, it will be possible to implement. Scientific Computing 15

16 A SOLUTION MADE WITH THE PTHREAD LIBRARY Scientific Computing 16

17 REFERENCES ATTILA s log: l ATTILA s distribution mail: OpenMP webpage: Scientific Computing 17