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Details: - Fixed an innocuous bug that manifested when running the testsuite on extremely small matrices with randomization via the "powers of 2 in narrow precision range" option enabled. When the randomization function emits a perfect 0.0 to fill a 1x1 matrix, the testsuite will then compute 0.0/0.0 during the normalization process, which leads to NaN residuals. The solution entails smarter implementaions of randv, randnv, randm, and randnm, each of which will compute the 1-norm of the vector or matrix in question. If the object has a 1-norm of 0.0, the object is re-randomized until the 1-norm is not 0.0. Thanks to Kiran Varaganti for reporting this issue (#413). - Updated the implementation of randm_unb_var1() so that it loops over a call to the randv_unb_var1() implementation directly rather than calling it indirectly via randv(). This was done to avoid the overhead of multiple calls to norm1v() when randomizing the rows/columns of a matrix. - Updated comments. Change-Id: I0e3d65ff97b26afde614da746e17ed33646839d1
176 lines
5.3 KiB
C
176 lines
5.3 KiB
C
/*
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BLIS
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An object-based framework for developing high-performance BLAS-like
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libraries.
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Copyright (C) 2014, The University of Texas at Austin
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Redistribution and use in source and binary forms, with or without
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modification, are permitted provided that the following conditions are
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met:
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- Redistributions of source code must retain the above copyright
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notice, this list of conditions and the following disclaimer.
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- Redistributions in binary form must reproduce the above copyright
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notice, this list of conditions and the following disclaimer in the
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documentation and/or other materials provided with the distribution.
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- Neither the name(s) of the copyright holder(s) nor the names of its
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contributors may be used to endorse or promote products derived
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from this software without specific prior written permission.
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THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
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"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
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LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
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A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
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HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
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SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
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LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
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DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
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THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
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(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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*/
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#ifndef BLIS_RANDNP2S_H
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#define BLIS_RANDNP2S_H
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// randnp2s
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#define bli_srandnp2s( a ) \
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{ \
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bli_drandnp2s( a ); \
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}
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#if 0
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#define bli_drandnp2s_prev( a ) \
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{ \
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const double m_max = 3.0; \
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const double m_max2 = m_max + 2.0; \
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double t; \
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double r_val; \
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\
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/* Compute a narrow-range power of two.
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For the purposes of commentary, we'll assume that m_max = 4. This
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represents the largest power of two we will use to generate the
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random numbers. */ \
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\
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/* Generate a random real number t on the interval: [0.0, 6.0]. */ \
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t = ( ( double ) rand() / ( double ) RAND_MAX ) * m_max2; \
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\
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/* Modify t to guarantee that is never equal to the upper bound of
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the interval (in this case, 6.0). */ \
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if ( t == m_max2 ) t = t - 1.0; \
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\
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/* Transform the interval into the set of integers, {0,1,2,3,4,5}. */ \
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t = floor( t ); \
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\
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/* Map values of t == 0 to a final value of 0. */ \
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if ( t == 0.0 ) r_val = 0.0; \
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else \
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{ \
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/* This case handles values of t = {1,2,3,4,5}. */ \
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\
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double s_exp, s_val; \
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\
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/* Compute two random numbers to determine the signs of the
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exponent and the end result. */ \
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PASTEMAC(d,rands)( s_exp ); \
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PASTEMAC(d,rands)( s_val ); \
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\
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/* Compute r_val = 2^s where s = +/-(t-1) = {-4,-3,-2,-1,0,1,2,3,4}. */ \
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if ( s_exp < 0.0 ) r_val = pow( 2.0, -(t - 1.0) ); \
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else r_val = pow( 2.0, t - 1.0 ); \
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\
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/* If our sign value is negative, our random power of two will
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be negative. */ \
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if ( s_val < 0.0 ) r_val = -r_val; \
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} \
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\
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/* Normalize by the largest possible positive value. */ \
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r_val = r_val / pow( 2.0, m_max ); \
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\
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/* r_val = 0, or +/-{2^-4, 2^-3, 2^-2, 2^-1, 2^0, 2^1, 2^2, 2^3, 2^4}. */ \
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/* NOTE: For single-precision macros, this assignment results in typecast
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down to float. */ \
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a = r_val; \
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}
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#endif
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#define bli_drandnp2s( a ) \
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{ \
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const double m_max = 6.0; \
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const double m_max2 = m_max + 2.0; \
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double t; \
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double r_val; \
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\
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/* Compute a narrow-range power of two.
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For the purposes of commentary, we'll assume that m_max = 4. This
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represents the largest power of two we will use to generate the
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random numbers. */ \
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\
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do \
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{ \
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/* Generate a random real number t on the interval: [0.0, 6.0]. */ \
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t = ( ( double ) rand() / ( double ) RAND_MAX ) * m_max2; \
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\
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/* Transform the interval into the set of integers, {0,1,2,3,4,5}.
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Note that 6 is prohibited by the loop guard below. */ \
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t = floor( t ); \
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} \
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/* If t is ever equal to m_max2, we re-randomize. The guard against
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m_max2 < t is for sanity and shouldn't happen, unless perhaps there
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is weirdness in the typecasting to double when computing t above. */ \
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while ( m_max2 <= t ); \
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\
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/* Map values of t == 0 to a final value of 0. */ \
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if ( t == 0.0 ) r_val = 0.0; \
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else \
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{ \
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/* This case handles values of t = {1,2,3,4,5}. */ \
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\
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double s_val; \
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\
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/* Compute r_val = 2^s where s = -(t-1) = {-4,-3,-2,-1,0}. */ \
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r_val = pow( 2.0, -(t - 1.0) ); \
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\
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/* Compute a random number to determine the sign of the final
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result. */ \
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PASTEMAC(d,rands)( s_val ); \
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\
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/* If our sign value is negative, our random power of two will
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be negative. */ \
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if ( s_val < 0.0 ) r_val = -r_val; \
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} \
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\
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/* r_val = 0, or +/-{2^0, 2^-1, 2^-2, 2^-3, 2^-4}. */ \
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/* NOTE: For single-precision macros, this assignment results in typecast
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down to float. */ \
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a = r_val; \
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}
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#define bli_crandnp2s( a ) \
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{ \
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float ar, ai; \
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\
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bli_srandnp2s( ar ); \
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bli_srandnp2s( ai ); \
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\
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bli_csets( ar, ai, (a) ); \
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}
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#define bli_zrandnp2s( a ) \
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{ \
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double ar, ai; \
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\
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bli_drandnp2s( ar ); \
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bli_drandnp2s( ai ); \
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\
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bli_zsets( ar, ai, (a) ); \
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}
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#endif
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