2080900985
git-svn-id: http://skia.googlecode.com/svn/trunk@1549 2bbb7eff-a529-9590-31e7-b0007b416f81
319 lines
9.3 KiB
C++
319 lines
9.3 KiB
C++
#include "SkBenchmark.h"
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#include "SkMatrix.h"
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#include "SkRandom.h"
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#include "SkString.h"
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class MatrixBench : public SkBenchmark {
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SkString fName;
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enum { N = 100000 };
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public:
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MatrixBench(void* param, const char name[]) : INHERITED(param) {
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fName.printf("matrix_%s", name);
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}
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virtual void performTest() = 0;
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protected:
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virtual int mulLoopCount() const { return 1; }
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virtual const char* onGetName() {
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return fName.c_str();
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}
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virtual void onDraw(SkCanvas* canvas) {
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int n = N * this->mulLoopCount();
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for (int i = 0; i < n; i++) {
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this->performTest();
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}
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}
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private:
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typedef SkBenchmark INHERITED;
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};
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// we want to stop the compiler from eliminating code that it thinks is a no-op
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// so we have a non-static global we increment, hoping that will convince the
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// compiler to execute everything
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int gMatrixBench_NonStaticGlobal;
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#define always_do(pred) \
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do { \
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if (pred) { \
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++gMatrixBench_NonStaticGlobal; \
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} \
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} while (0)
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class EqualsMatrixBench : public MatrixBench {
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public:
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EqualsMatrixBench(void* param) : INHERITED(param, "equals") {}
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protected:
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virtual void performTest() {
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SkMatrix m0, m1, m2;
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m0.reset();
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m1.reset();
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m2.reset();
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always_do(m0 == m1);
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always_do(m1 == m2);
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always_do(m2 == m0);
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always_do(m0.getType());
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always_do(m1.getType());
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always_do(m2.getType());
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}
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private:
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typedef MatrixBench INHERITED;
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};
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class ScaleMatrixBench : public MatrixBench {
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public:
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ScaleMatrixBench(void* param) : INHERITED(param, "scale") {
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fSX = fSY = SkFloatToScalar(1.5f);
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fM0.reset();
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fM1.setScale(fSX, fSY);
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fM2.setTranslate(fSX, fSY);
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}
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protected:
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virtual void performTest() {
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SkMatrix m;
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m = fM0; m.preScale(fSX, fSY);
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m = fM1; m.preScale(fSX, fSY);
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m = fM2; m.preScale(fSX, fSY);
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}
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private:
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SkMatrix fM0, fM1, fM2;
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SkScalar fSX, fSY;
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typedef MatrixBench INHERITED;
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};
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// having unknown values in our arrays can throw off the timing a lot, perhaps
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// handling NaN values is a lot slower. Anyway, this guy is just meant to put
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// reasonable values in our arrays.
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template <typename T> void init9(T array[9]) {
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SkRandom rand;
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for (int i = 0; i < 9; i++) {
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array[i] = rand.nextSScalar1();
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}
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}
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// Test the performance of setConcat() non-perspective case:
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// using floating point precision only.
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class FloatConcatMatrixBench : public MatrixBench {
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public:
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FloatConcatMatrixBench(void* p) : INHERITED(p, "concat_floatfloat") {
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init9(mya);
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init9(myb);
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init9(myr);
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}
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protected:
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virtual int mulLoopCount() const { return 4; }
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static inline void muladdmul(float a, float b, float c, float d,
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float* result) {
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*result = a * b + c * d;
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}
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virtual void performTest() {
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const float* a = mya;
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const float* b = myb;
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float* r = myr;
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muladdmul(a[0], b[0], a[1], b[3], &r[0]);
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muladdmul(a[0], b[1], a[1], b[4], &r[1]);
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muladdmul(a[0], b[2], a[1], b[5], &r[2]);
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r[2] += a[2];
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muladdmul(a[3], b[0], a[4], b[3], &r[3]);
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muladdmul(a[3], b[1], a[4], b[4], &r[4]);
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muladdmul(a[3], b[2], a[4], b[5], &r[5]);
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r[5] += a[5];
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r[6] = r[7] = 0.0f;
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r[8] = 1.0f;
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}
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private:
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float mya [9];
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float myb [9];
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float myr [9];
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typedef MatrixBench INHERITED;
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};
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static inline float SkDoubleToFloat(double x) {
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return static_cast<float>(x);
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}
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// Test the performance of setConcat() non-perspective case:
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// using floating point precision but casting up to float for
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// intermediate results during computations.
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class FloatDoubleConcatMatrixBench : public MatrixBench {
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public:
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FloatDoubleConcatMatrixBench(void* p) : INHERITED(p, "concat_floatdouble") {
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init9(mya);
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init9(myb);
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init9(myr);
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}
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protected:
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virtual int mulLoopCount() const { return 4; }
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static inline void muladdmul(float a, float b, float c, float d,
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float* result) {
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*result = SkDoubleToFloat((double)a * b + (double)c * d);
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}
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virtual void performTest() {
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const float* a = mya;
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const float* b = myb;
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float* r = myr;
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muladdmul(a[0], b[0], a[1], b[3], &r[0]);
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muladdmul(a[0], b[1], a[1], b[4], &r[1]);
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muladdmul(a[0], b[2], a[1], b[5], &r[2]);
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r[2] += a[2];
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muladdmul(a[3], b[0], a[4], b[3], &r[3]);
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muladdmul(a[3], b[1], a[4], b[4], &r[4]);
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muladdmul(a[3], b[2], a[4], b[5], &r[5]);
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r[5] += a[5];
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r[6] = r[7] = 0.0f;
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r[8] = 1.0f;
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}
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private:
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float mya [9];
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float myb [9];
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float myr [9];
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typedef MatrixBench INHERITED;
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};
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// Test the performance of setConcat() non-perspective case:
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// using double precision only.
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class DoubleConcatMatrixBench : public MatrixBench {
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public:
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DoubleConcatMatrixBench(void* p) : INHERITED(p, "concat_double") {
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init9(mya);
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init9(myb);
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init9(myr);
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}
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protected:
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virtual int mulLoopCount() const { return 4; }
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static inline void muladdmul(double a, double b, double c, double d,
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double* result) {
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*result = a * b + c * d;
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}
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virtual void performTest() {
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const double* a = mya;
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const double* b = myb;
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double* r = myr;
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muladdmul(a[0], b[0], a[1], b[3], &r[0]);
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muladdmul(a[0], b[1], a[1], b[4], &r[1]);
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muladdmul(a[0], b[2], a[1], b[5], &r[2]);
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r[2] += a[2];
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muladdmul(a[3], b[0], a[4], b[3], &r[3]);
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muladdmul(a[3], b[1], a[4], b[4], &r[4]);
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muladdmul(a[3], b[2], a[4], b[5], &r[5]);
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r[5] += a[5];
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r[6] = r[7] = 0.0;
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r[8] = 1.0;
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}
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private:
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double mya [9];
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double myb [9];
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double myr [9];
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typedef MatrixBench INHERITED;
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};
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#ifdef SK_SCALAR_IS_FLOAT
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class ScaleTransMixedMatrixBench : public MatrixBench {
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public:
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ScaleTransMixedMatrixBench(void* p) : INHERITED(p, "scaletrans_mixed"), fCount (16) {
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fMatrix.setAll(fRandom.nextS(), fRandom.nextS(), fRandom.nextS(),
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fRandom.nextS(), fRandom.nextS(), fRandom.nextS(),
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fRandom.nextS(), fRandom.nextS(), fRandom.nextS());
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int i;
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for (i = 0; i < fCount; i++) {
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fSrc[i].fX = fRandom.nextS();
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fSrc[i].fY = fRandom.nextS();
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fDst[i].fX = fRandom.nextS();
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fDst[i].fY = fRandom.nextS();
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}
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}
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protected:
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virtual void performTest() {
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SkPoint* dst = fDst;
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const SkPoint* src = fSrc;
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int count = fCount;
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float mx = fMatrix[SkMatrix::kMScaleX];
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float my = fMatrix[SkMatrix::kMScaleY];
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float tx = fMatrix[SkMatrix::kMTransX];
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float ty = fMatrix[SkMatrix::kMTransY];
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do {
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dst->fY = SkScalarMulAdd(src->fY, my, ty);
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dst->fX = SkScalarMulAdd(src->fX, mx, tx);
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src += 1;
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dst += 1;
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} while (--count);
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}
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private:
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SkMatrix fMatrix;
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SkPoint fSrc [16];
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SkPoint fDst [16];
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int fCount;
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SkRandom fRandom;
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typedef MatrixBench INHERITED;
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};
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class ScaleTransDoubleMatrixBench : public MatrixBench {
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public:
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ScaleTransDoubleMatrixBench(void* p) : INHERITED(p, "scaletrans_double"), fCount (16) {
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init9(fMatrix);
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int i;
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for (i = 0; i < fCount; i++) {
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fSrc[i].fX = fRandom.nextS();
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fSrc[i].fY = fRandom.nextS();
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fDst[i].fX = fRandom.nextS();
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fDst[i].fY = fRandom.nextS();
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}
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}
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protected:
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virtual void performTest() {
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SkPoint* dst = fDst;
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const SkPoint* src = fSrc;
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int count = fCount;
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// As doubles, on Z600 Linux systems this is 2.5x as expensive as mixed mode
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float mx = fMatrix[SkMatrix::kMScaleX];
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float my = fMatrix[SkMatrix::kMScaleY];
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float tx = fMatrix[SkMatrix::kMTransX];
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float ty = fMatrix[SkMatrix::kMTransY];
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do {
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dst->fY = src->fY * my + ty;
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dst->fX = src->fX * mx + tx;
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src += 1;
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dst += 1;
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} while (--count);
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}
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private:
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double fMatrix [9];
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SkPoint fSrc [16];
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SkPoint fDst [16];
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int fCount;
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SkRandom fRandom;
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typedef MatrixBench INHERITED;
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};
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#endif
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static SkBenchmark* M0(void* p) { return new EqualsMatrixBench(p); }
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static SkBenchmark* M1(void* p) { return new ScaleMatrixBench(p); }
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static SkBenchmark* M2(void* p) { return new FloatConcatMatrixBench(p); }
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static SkBenchmark* M3(void* p) { return new FloatDoubleConcatMatrixBench(p); }
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static SkBenchmark* M4(void* p) { return new DoubleConcatMatrixBench(p); }
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static BenchRegistry gReg0(M0);
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static BenchRegistry gReg1(M1);
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static BenchRegistry gReg2(M2);
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static BenchRegistry gReg3(M3);
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static BenchRegistry gReg4(M4);
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#ifdef SK_SCALAR_IS_FLOAT
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static SkBenchmark* FlM0(void* p) { return new ScaleTransMixedMatrixBench(p); }
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static SkBenchmark* FlM1(void* p) { return new ScaleTransDoubleMatrixBench(p); }
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static BenchRegistry gFlReg5(FlM0);
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static BenchRegistry gFlReg6(FlM1);
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#endif
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