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Multithreaded SGEMV var 1 with smart threading
- Implemented an OpenMP based stand alone SGEMV kernel for row-major (var 1) for multithread scenarios - Smart threading is enabled when AOCL DYNAMIC is defined - Number of threads are decided based on the input dims using smart threading AMD-Internal: [CPUPL-1984] Change-Id: I9b191e965ba7468e95aabcce21b35a533017502e
This commit is contained in:
S, HariharaSudhan
committed by
Dipal M Zambare
Dipal M Zambare
parent
16de63c818
commit
a8bc55c373
@@ -332,6 +332,92 @@ void bli_dgemv_unf_var1
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AOCL_DTL_TRACE_EXIT(AOCL_DTL_LEVEL_TRACE_3);
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}
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// Returns the optimal number of threads for the given input sizes and fuse factor
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void bli_sgemv_var1_smart_threading
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(
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dim_t m, dim_t n,
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dim_t fuse,
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dim_t* nt, dim_t nt_max
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)
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{
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// Calculate the amount data processed per iteration
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dim_t n_per_loop = n / fuse;
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double data_per_iter = n_per_loop* m;
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double m_n_ratio = m/n;
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// When the input value is less than the fuse factor
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if(n_per_loop < 1)
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{
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*nt = 1;
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return;
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}
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// Then there are two cases one
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// In m < n the thread spawning is less aggressive when compared to m > n and m = n cases
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if(m_n_ratio <= 0.6)
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{
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// Boundary units is the amount of data processed by each iteration
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// This is the variable X in the equation
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const double lower_boundary = 50000;
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const double higher_boundary = 500000;
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if(data_per_iter < lower_boundary)
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{
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double coeff_x = 0.9148;
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double constant = -1.6252;
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// Number of threads = 0.9148 * log(x) - 1.6252
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*nt = ceil(coeff_x * log(data_per_iter) + constant);
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}
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else if(data_per_iter < higher_boundary)
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{
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float coeff_x = 10.23;
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float constant = -82.332;
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// Number of threads = 10.23 * log(x) - 82.332
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*nt = ceil(coeff_x * log(data_per_iter) + constant);
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}
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else
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{
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// When the amount of data to be processed is above both of the boundaries
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// The number of threads spawned will be equal to the max number of threads set
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*nt = nt_max;
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}
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}
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else
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{
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// Boundary units is the amount of data processed by each iteration
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// This is the variable X in the equation
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const float lower_boundary = 50000;
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const float higher_boundary = 360000;
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if(data_per_iter < lower_boundary)
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{
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float coeff_x2 = -2E-09;
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float coeff_x = 0.0002;
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float constant = 1.0234;
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// Number of threads = -2E-09*x^2 + 0.0002 * x + 1.0234
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*nt = ceil(coeff_x2 * (data_per_iter * data_per_iter) + coeff_x * data_per_iter + constant);
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}
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else if(data_per_iter < higher_boundary)
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{
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float coeff_x = 16.917;
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float constant = -164.82;
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// Number of threads = 16.917 * log(x) - 164.82
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*nt = ceil(coeff_x * log(data_per_iter) + constant);
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}
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else
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{
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// When the amount of data to be processed is above both of the boundaries
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// The number of threads spawned will be equal to the max number of threads set
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*nt = nt_max;
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}
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}
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// When the number of threads calculated is greater than the user provided value
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// Choose the user provided value
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if(*nt > nt_max)
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*nt = nt_max;
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}
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void bli_sgemv_unf_var1
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(
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trans_t transa,
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@@ -407,7 +493,46 @@ void bli_sgemv_unf_var1
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return;
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}
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/* Query the context for the kernel function pointer and fusing factor. */
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// If both multithreading and OpenMP are enabled, GEMV will multithread
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#if defined(BLIS_ENABLE_MULTITHREADING) && defined(BLIS_ENABLE_OPENMP)
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dim_t nt, nt_max;
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rntm_t rnmt_obj;
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b_fuse = 4;
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// Initialize a local runtime with global settings.
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bli_rntm_init_from_global( &rnmt_obj );
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// Query the total number of threads from the rntm_t object.
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nt_max = bli_rntm_num_threads( &rnmt_obj );
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//Setting the thread count to the maximum number of threads provided
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nt = nt_max;
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// Enable smart threading when AOCL dynamic is enabled
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#ifdef AOCL_DYNAMIC
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bli_sgemv_var1_smart_threading(n_elem, n_iter, b_fuse, &nt, nt_max);
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#endif
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// Pass the input paramaters along with the number of threads to be used
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bli_multi_sgemv_4x2
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(
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conja,
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conjx,
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n_elem,
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n_iter,
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alpha,
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a, cs_at, rs_at,
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x, incx,
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beta,
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y, incy,
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cntx,
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nt
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);
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#else
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b_fuse = 8;
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for ( i = 0; i < n_iter; i += f )
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@@ -434,6 +559,7 @@ void bli_sgemv_unf_var1
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);
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}
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#endif
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}
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INSERT_GENTFUNC_BASIC0_CZ( gemv_unf_var1 )
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@@ -35,6 +35,24 @@
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#include "immintrin.h"
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#include "blis.h"
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/* Union data structure to access AVX registers
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One 256-bit AVX register holds 8 SP elements. */
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typedef union
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{
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__m256 v;
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float f[8] __attribute__((aligned(64)));
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} v8sf_t;
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/* Union data structure to access AVX registers
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* One 128-bit AVX register holds 4 SP elements. */
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typedef union
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{
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__m128 v;
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float f[4] __attribute__((aligned(64)));
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} v4sf_t;
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/*
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This implementation uses 512 bits of cache line efficiently for
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column stored matrix and vectors.
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@@ -477,3 +495,380 @@ void bli_cgemv_zen_int_4x4
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}
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}
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/*
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Function performs multithreaded GEMV for float datatype
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All parameters are similar to single thread GEMV except
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n_thread which specifies the number of threads to be used
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*/
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void bli_multi_sgemv_4x2
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(
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conj_t conjat,
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conj_t conjx,
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dim_t m,
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dim_t b_n,
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float* restrict alpha,
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float* restrict a, inc_t inca, inc_t lda,
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float* restrict x, inc_t incx,
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float* restrict beta,
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float* restrict y, inc_t incy,
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cntx_t* restrict cntx,
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dim_t n_threads
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)
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{
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const dim_t b_fuse = 4;
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const dim_t n_elem_per_reg = 8;
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dim_t total_iteration = 0;
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// If the b_n dimension is zero, y is empty and there is no computation.
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if (bli_zero_dim1(b_n))
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return;
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// If the m dimension is zero, or if alpha is zero, the computation
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// simplifies to updating y.
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if (bli_zero_dim1(m) || PASTEMAC(s, eq0)(*alpha))
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{
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bli_sscalv_zen_int10(
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BLIS_NO_CONJUGATE,
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b_n,
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beta,
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y, incy,
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cntx);
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return;
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}
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// If b_n is not equal to the fusing factor, then perform the entire
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// operation as a loop over dotxv.
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if (b_n < b_fuse)
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{
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for (dim_t i = 0; i < b_n; ++i)
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{
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float *a1 = a + (0) * inca + (i)*lda;
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float *x1 = x + (0) * incx;
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float *psi1 = y + (i)*incy;
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bli_sdotxv_zen_int(
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conjat,
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conjx,
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m,
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alpha,
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a1, inca,
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x1, incx,
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beta,
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psi1,
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cntx);
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}
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return;
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}
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// Calculate the total number of multithreaded iteration
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total_iteration = b_n / b_fuse;
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#pragma omp parallel for num_threads(n_threads)
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for (dim_t j = 0; j < total_iteration; j++)
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{
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float *A1 = a + (b_fuse * j) * lda;
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float *x1 = x;
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float *y1 = y + (b_fuse * j) * incy;
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// Intermediate variables to hold the completed dot products
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float rho0[4] = {0, 0, 0, 0};
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// If vectorization is possible, perform them with vector
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// instructions.
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if (inca == 1 && incx == 1)
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{
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const dim_t n_iter_unroll = 2;
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// Use the unrolling factor and the number of elements per register
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// to compute the number of vectorized and leftover iterations.
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dim_t l, unroll_inc, m_viter[2], m_left = m;
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unroll_inc = n_elem_per_reg * n_iter_unroll;
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m_viter[0] = m_left / unroll_inc;
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m_left = m_left % unroll_inc;
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m_viter[1] = m_left / n_elem_per_reg ;
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m_left = m_left % n_elem_per_reg;
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// Set up pointers for x and the b_n columns of A (rows of A^T).
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float *restrict x0 = x1;
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float *restrict av[4];
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av[0] = A1 + 0 * lda;
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av[1] = A1 + 1 * lda;
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av[2] = A1 + 2 * lda;
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av[3] = A1 + 3 * lda;
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// Initialize b_n rho vector accumulators to zero.
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v8sf_t rhov[4];
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rhov[0].v = _mm256_setzero_ps();
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rhov[1].v = _mm256_setzero_ps();
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rhov[2].v = _mm256_setzero_ps();
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rhov[3].v = _mm256_setzero_ps();
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v8sf_t xv[2];
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v8sf_t a_vec[8];
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// FMA operation is broken down to mul and add
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// to reduce backend stalls
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for (l = 0; l < m_viter[0]; ++l)
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{
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xv[0].v = _mm256_loadu_ps(x0);
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x0 += n_elem_per_reg;
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xv[1].v = _mm256_loadu_ps(x0);
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x0 += n_elem_per_reg;
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a_vec[0].v = _mm256_loadu_ps(av[0]);
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a_vec[4].v = _mm256_loadu_ps(av[0] + n_elem_per_reg);
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// perform: rho?v += a?v * x0v;
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a_vec[0].v = _mm256_mul_ps(a_vec[0].v, xv[0].v);
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rhov[0].v = _mm256_fmadd_ps(a_vec[4].v, xv[1].v, rhov[0].v);
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rhov[0].v = _mm256_add_ps(a_vec[0].v, rhov[0].v);
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a_vec[1].v = _mm256_loadu_ps(av[1]);
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a_vec[5].v = _mm256_loadu_ps(av[1] + n_elem_per_reg);
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a_vec[1].v = _mm256_mul_ps(a_vec[1].v, xv[0].v);
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rhov[1].v = _mm256_fmadd_ps(a_vec[5].v, xv[1].v, rhov[1].v);
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rhov[1].v = _mm256_add_ps(a_vec[1].v, rhov[1].v);
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a_vec[2].v = _mm256_loadu_ps(av[2]);
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a_vec[6].v = _mm256_loadu_ps(av[2] + n_elem_per_reg);
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a_vec[2].v = _mm256_mul_ps(a_vec[2].v, xv[0].v);
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rhov[2].v = _mm256_fmadd_ps(a_vec[6].v, xv[1].v, rhov[2].v);
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rhov[2].v = _mm256_add_ps(a_vec[2].v, rhov[2].v);
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a_vec[3].v = _mm256_loadu_ps(av[3]);
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a_vec[7].v = _mm256_loadu_ps(av[3] + n_elem_per_reg);
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a_vec[3].v = _mm256_mul_ps(a_vec[3].v, xv[0].v);
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rhov[3].v = _mm256_fmadd_ps(a_vec[7].v, xv[1].v, rhov[3].v);
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rhov[3].v = _mm256_add_ps(a_vec[3].v, rhov[3].v);
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av[0] += unroll_inc;
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av[1] += unroll_inc;
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av[2] += unroll_inc;
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av[3] += unroll_inc;
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}
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for (l = 0; l < m_viter[1]; ++l)
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{
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// Load the input values.
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xv[0].v = _mm256_loadu_ps(x0);
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x0 += n_elem_per_reg;
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a_vec[0].v = _mm256_loadu_ps(av[0]);
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a_vec[1].v = _mm256_loadu_ps(av[1]);
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rhov[0].v = _mm256_fmadd_ps(a_vec[0].v, xv[0].v, rhov[0].v);
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rhov[1].v = _mm256_fmadd_ps(a_vec[1].v, xv[0].v, rhov[1].v);
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av[0] += n_elem_per_reg;
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av[1] += n_elem_per_reg;
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a_vec[2].v = _mm256_loadu_ps(av[2]);
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a_vec[3].v = _mm256_loadu_ps(av[3]);
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rhov[2].v = _mm256_fmadd_ps(a_vec[2].v, xv[0].v, rhov[2].v);
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rhov[3].v = _mm256_fmadd_ps(a_vec[3].v, xv[0].v, rhov[3].v);
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av[2] += n_elem_per_reg;
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av[3] += n_elem_per_reg;
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}
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// Sum the elements within each vector.
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// Sum the elements of a given rho?v with hadd.
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rhov[0].v = _mm256_hadd_ps(rhov[0].v, rhov[1].v);
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rhov[2].v = _mm256_hadd_ps(rhov[2].v, rhov[3].v);
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rhov[0].v = _mm256_hadd_ps(rhov[0].v, rhov[0].v);
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rhov[2].v = _mm256_hadd_ps(rhov[2].v, rhov[2].v);
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// Manually add the results from above to finish the sum.
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rho0[0] = rhov[0].f[0] + rhov[0].f[4];
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rho0[1] = rhov[0].f[1] + rhov[0].f[5];
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rho0[2] = rhov[2].f[0] + rhov[2].f[4];
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rho0[3] = rhov[2].f[1] + rhov[2].f[5];
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// If leftover elements are more than 4, perform SSE
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if (m_left > 4)
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{
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v4sf_t xv128, a_vec128[4], rhov128[4];
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rhov128[0].v = _mm_set1_ps(0);
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rhov128[1].v = _mm_set1_ps(0);
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rhov128[2].v = _mm_set1_ps(0);
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rhov128[3].v = _mm_set1_ps(0);
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// Load the input values.
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xv128.v = _mm_loadu_ps(x0 + 0 * n_elem_per_reg);
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x0 += 4;
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m_left -= 4;
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a_vec128[0].v = _mm_loadu_ps(av[0]);
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a_vec128[1].v = _mm_loadu_ps(av[1]);
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// perform: rho?v += a?v * x0v;
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rhov128[0].v = _mm_fmadd_ps(a_vec128[0].v, xv128.v, rhov128[0].v);
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rhov128[1].v = _mm_fmadd_ps(a_vec128[1].v, xv128.v, rhov128[1].v);
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rhov128[0].v = _mm_hadd_ps(rhov128[0].v, rhov128[1].v);
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rhov128[0].v = _mm_hadd_ps(rhov128[0].v, rhov128[0].v);
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a_vec128[2].v = _mm_loadu_ps(av[2]);
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a_vec128[3].v = _mm_loadu_ps(av[3]);
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rhov128[2].v = _mm_fmadd_ps(a_vec128[2].v, xv128.v, rhov128[2].v);
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rhov128[3].v = _mm_fmadd_ps(a_vec128[3].v, xv128.v, rhov128[3].v);
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rhov128[2].v = _mm_hadd_ps(rhov128[2].v, rhov128[3].v);
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rhov128[2].v = _mm_hadd_ps(rhov128[2].v, rhov128[2].v);
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rho0[0] += rhov128[0].f[0];
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rho0[1] += rhov128[0].f[1];
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rho0[2] += rhov128[2].f[0];
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rho0[3] += rhov128[2].f[1];
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av[0] += 4;
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av[1] += 4;
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av[2] += 4;
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av[3] += 4;
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}
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// If there are leftover iterations, perform them with scalar code.
|
||||
for (l = 0; l < m_left; ++l)
|
||||
{
|
||||
rho0[0] += *(av[0]) * (*x0);
|
||||
rho0[1] += *(av[1]) * (*x0);
|
||||
rho0[2] += *(av[2]) * (*x0);
|
||||
rho0[3] += *(av[3]) * (*x0);
|
||||
|
||||
x0 += incx;
|
||||
av[0] += inca;
|
||||
av[1] += inca;
|
||||
av[2] += inca;
|
||||
av[3] += inca;
|
||||
}
|
||||
|
||||
}
|
||||
else
|
||||
{
|
||||
// When vectorization is not possible, perform with scalar code
|
||||
|
||||
// Initialize pointers for x and the b_n columns of A (rows of A^T).
|
||||
float *restrict x0 = x1;
|
||||
float *restrict a0 = A1 + 0 * lda;
|
||||
float *restrict a1 = A1 + 1 * lda;
|
||||
float *restrict a2 = A1 + 2 * lda;
|
||||
float *restrict a3 = A1 + 3 * lda;
|
||||
|
||||
for (dim_t l = 0; l < m; ++l)
|
||||
{
|
||||
const float x0c = *x0;
|
||||
|
||||
const float a0c = *a0;
|
||||
const float a1c = *a1;
|
||||
const float a2c = *a2;
|
||||
const float a3c = *a3;
|
||||
|
||||
rho0[0] += a0c * x0c;
|
||||
rho0[1] += a1c * x0c;
|
||||
rho0[2] += a2c * x0c;
|
||||
rho0[3] += a3c * x0c;
|
||||
|
||||
x0 += incx;
|
||||
a0 += inca;
|
||||
a1 += inca;
|
||||
a2 += inca;
|
||||
a3 += inca;
|
||||
}
|
||||
}
|
||||
|
||||
v4sf_t rho0v, y0v;
|
||||
|
||||
rho0v.v = _mm_loadu_ps(rho0);
|
||||
|
||||
// Broadcast the alpha scalar.
|
||||
v4sf_t alphav;
|
||||
alphav.v = _mm_broadcast_ss(alpha);
|
||||
|
||||
// We know at this point that alpha is nonzero; however, beta may still
|
||||
// be zero. If beta is indeed zero, we must overwrite y rather than scale
|
||||
// by beta (in case y contains NaN or Inf).
|
||||
if (PASTEMAC(s, eq0)(*beta))
|
||||
{
|
||||
// Apply alpha to the accumulated dot product in rho:
|
||||
// y := alpha * rho
|
||||
y0v.v = _mm_mul_ps(alphav.v, rho0v.v);
|
||||
}
|
||||
else
|
||||
{
|
||||
// Broadcast the beta scalar.
|
||||
v4sf_t betav;
|
||||
betav.v = _mm_broadcast_ss(beta);
|
||||
|
||||
if (incy == 0)
|
||||
{
|
||||
// Load y.
|
||||
y0v.v = _mm_loadu_ps(y1 + 0 * n_elem_per_reg);
|
||||
}
|
||||
else
|
||||
{
|
||||
// Load y.
|
||||
y0v.f[0] = *(y1 + 0 * incy);
|
||||
y0v.f[1] = *(y1 + 1 * incy);
|
||||
y0v.f[2] = *(y1 + 2 * incy);
|
||||
y0v.f[3] = *(y1 + 3 * incy);
|
||||
}
|
||||
|
||||
// Apply beta to y and alpha to the accumulated dot product in rho:
|
||||
// y := beta * y + alpha * rho
|
||||
y0v.v = _mm_mul_ps(betav.v, y0v.v);
|
||||
y0v.v = _mm_fmadd_ps(alphav.v, rho0v.v, y0v.v);
|
||||
}
|
||||
|
||||
// Store the output.
|
||||
if (incy == 1)
|
||||
{
|
||||
_mm_storeu_ps((y1 + 0 * n_elem_per_reg), y0v.v);
|
||||
}
|
||||
else
|
||||
{
|
||||
// Store the output.
|
||||
*(y1 + 0 * incy) = y0v.f[0];
|
||||
*(y1 + 1 * incy) = y0v.f[1];
|
||||
*(y1 + 2 * incy) = y0v.f[2];
|
||||
*(y1 + 3 * incy) = y0v.f[3];
|
||||
}
|
||||
}
|
||||
|
||||
// Performs the complete computation if OpenMP is not enabled
|
||||
dim_t start = total_iteration * b_fuse;
|
||||
dim_t new_fuse = 8, f;
|
||||
|
||||
// Left over corner cases completed using fused kernel
|
||||
for (dim_t i = start; i < b_n; i += f)
|
||||
{
|
||||
f = bli_determine_blocksize_dim_f(i, b_n, new_fuse);
|
||||
|
||||
float *A1 = a + (i)*lda + (0) * inca;
|
||||
float *x1 = x + (0) * incx;
|
||||
float *y1 = y + (i)*incy;
|
||||
|
||||
/* y1 = beta * y1 + alpha * A1 * x; */
|
||||
bli_sdotxf_zen_int_8(
|
||||
conjat,
|
||||
conjx,
|
||||
m,
|
||||
f,
|
||||
alpha,
|
||||
A1, inca, lda,
|
||||
x1, incx,
|
||||
beta,
|
||||
y1, incy,
|
||||
cntx);
|
||||
}
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user