By John Levesque, Gene Wagenbreth
High functionality Computing: Programming and Applications provides ideas that handle new functionality concerns within the programming of excessive functionality computing (HPC) functions. Omitting tedious information, the e-book discusses structure thoughts and programming suggestions which are the main pertinent to software builders for reaching excessive functionality. even supposing the textual content concentrates on C and Fortran, the options defined may be utilized to different languages, equivalent to C++ and Java.
Drawing on their event with chips from AMD and structures, interconnects, and software program from Cray Inc., the authors discover the issues that create bottlenecks in achieving solid functionality. They conceal concepts that pertain to every of the 3 degrees of parallelism:
• Message passing among the nodes
• Shared reminiscence parallelism at the nodes or the a number of guide, a number of info (MIMD) devices at the accelerator
• Vectorization at the internal point
After discussing architectural and software program demanding situations, the e-book outlines a method for porting and optimizing an present program to a wide hugely parallel processor (MPP) method. With a glance towards the longer term, it additionally introduces using normal objective pics processing devices (GPGPUs) for accomplishing HPC computations. A significant other web site at www.hybridmulticoreoptimization.com includes all of the examples from the e-book, in addition to up-to-date timing effects at the most modern published processors.
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Extra resources for High Performance Computing: Programming and Applications (Chapman & Hall/CRC Computational Science)
Unfortunately, since Fortran does not prohibit this type of coding, the alignment and padding of arrays by compilers is very limited. A compiler can pad and modify the alignment of arrays when they are allocated a s a utomatic o r l ocal d ata. I n t his c ase t he co mpiler a llocates memory and adds padding if necessary to properly align the array for efficient access. Unfortunately, t he inhibitors to a lignment a nd padd ing fa r outnumber these cases. The application developer should accept responsibility for allocating their arrays in such a way that the alignment is conducive t o effective memor y ut ilization.
X displacement REAL(KIND(0D0)) Uy ! Y displacement REAL(KIND(0D0)) Uz ! Z displacement REAL(KIND(0D0)) Vx ! X velocity REAL(KIND(0D0)) Vy ! Y velocity REAL(KIND(0D0)) Vz ! Z velocity REAL(KIND(0D0)) Ax ! X acceleration REAL(KIND(0D0)) Ay ! Y acceleration REAL(KIND(0D0)) Az ! Z acceleration END TYPE TYPE (motion_type), DIMENSION(:), ALLOCATABLE :: MOTION Rather than the arrays being dimensioned within the derived type, the derived t ype is d imensioned. This results i n each of t he a rrays having a stride of 12, which is hurting both TLB and cache utilization.
One is to generate a code that computes all values of the loop index and then only How Compilers Optimize Programs ◾ 33 store results when the IF condition is true. This is called a controlled store. 0 where true. If C(I) is never true, this will give extremely poor performance. If, on the other hand, a majority of the conditions are true, the benefit can be significant. Control stored treatment of IF statements has a problem in that the condition could be hiding a singularity. 0)B(I) = SQRT(A(I)) Here, the SQRT(A(I)) is not defined when A(I) is less than zero.
High Performance Computing: Programming and Applications (Chapman & Hall/CRC Computational Science) by John Levesque, Gene Wagenbreth