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lattice qcd is a powerful method to study quantum chromodynamics ( qcd ) in a nonperturbative way . in lattice qcd , a path integral is directly evaluated on a discrete space - time lattice by means of the monte carlo method . as computer technology advances pc clusters can also be used for lattice qcd simulations as well as a number of commercial supercomputers . since lattice qcd simulations demand huge computer power , it is very important to optimize the simulation codes so as to exploit the full potential of the processor . thus we optimize the hot spots of the codes such as the operation of a dirac operator to a spinor ( referred as @xmath0 hereafter ) and linear algebra of spinors , e.g.    * @xmath1 : scalar product of two spinors * @xmath2 : norm square of a spinor * @xmath3 : add two spinors with a constant factor and assign the result to one of the source spinors    in the simulation , a spinor is defined as a vector with 12 complex components on each grid point ( site ) of the lattice , and a gauge field is defined as a complex @xmath4 matrix on a link , which connects nearest neighbor sites . numerically the operation @xmath0 is a combination of a complex @xmath4 matrix times a complex vector . from a technical point of view , we should optimize these two applications separately since @xmath0 is processor limited as it needs more than one thousand floating point operations per site , while the linear algebra routines demand more data transfer than actual computations ( memory limited ) .    in this note we report our experience of the optimization of the lattice qcd codes for the new pc cluster with the amd opteron processor  @xcite which is recently installed at desy hamburg . of course the parallel computing with multiple processors is of our interest , however , we have optimized here only single processor version as a first step . we also compare benchmarks with an older pc cluster with the intel xeon processors ( see table  [ tbl : spec ] for the specification of these two processors ) . .comparison of the specification of the two processors which we use [ cols="^,^,^",options="header " , ]     [ tbl : comp ]    for linear algebra we have more gain as expected from the difference of the processor clock speed . the larger data cache and the improvement of memory bandwidth may reflect the result since linear algebra routines are rather memory limited . in this report , we discuss the details of the optimization of the lattice qcd codes for the amd opteron processor . tuning of prefetch distance for each application can improve performance by more than a factor of two . the prefetch instruction , however , may cause significant slowing down in some linear algebra routines at certain lattice sizes due to bank conflict . we found that this slowing down can be cured by adopting two different prefetch distances for each source field . the effect of the modification in sse instructions can be observed only after the adjustment of the prefetch instructions . large cache effect is observed not only for the linear algebra routines but also for the @xmath0 routines when the source fields can fit into relatively large l2 data cache of the opteron processor . this suggests that the pc cluster of the opteron processor can achieve high performance when the sublattice size becomes small enough . as a next step of this work , we will develop a parallel version of the codes . the author would like to thank h. wittig and y. koma for a number of useful discussions . the author appreciate the original codes and the benchmark programs written by l.  giusti , m.  lscher and h.  wittig , which is the basis of the improvement reported here .      9 software optimization guide for amd athlon 64 and amd opteron processors ( publication  # : 25112 , issue date november 2004 ) amd64 architecture programmer s manual vol . 4 ( publication  # : 24592 issue date september 2003 ) sandeep.s , gcc - inline - assembly - howto ( ` http://www.ibiblio.org/gferg/ldp/ gcc-inline-assembly-howto.html ` ) + v0.1 , 01 march 2003 .
we report our experience of the optimization of the lattice qcd codes for the new opteron cluster at desy hamburg , including benchmarks . 
 details of the optimization using sse / sse2 instructions and the effective use of prefetch instructions are discussed .