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1 change: 0 additions & 1 deletion include/isaac/jit/generation/gemm.h
Original file line number Diff line number Diff line change
Expand Up @@ -79,7 +79,6 @@ class intelblas_gemm_image : public external_base
bool init_;
};


class gemm : public parameterized_base
{
private:
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735 changes: 440 additions & 295 deletions lib/jit/generation/gemm.cpp

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2 changes: 1 addition & 1 deletion lib/runtime/database/intel/json/skylake.json

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163,933 changes: 75,004 additions & 88,929 deletions lib/runtime/database/intel/skylake.hpp

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8 changes: 4 additions & 4 deletions tune/android/tune/tools.py
Original file line number Diff line number Diff line change
Expand Up @@ -131,22 +131,22 @@ def external_profiles(template):
res = []
if template is sc.templates.gemm_nn:
res += [sc.templates.cublas_gemm('N','N')]
if 'float32' in dtype.__name__:
if 'float64' not in dtype.__name__:
res += [sc.templates.intelblas_gemm('N','N')]
res += [sc.templates.intelblas_gemm_image('N','N')]
elif template is sc.templates.gemm_tn:
res += [sc.templates.cublas_gemm('T','N')]
if 'float32' in dtype.__name__:
if 'float64' not in dtype.__name__:
res += [sc.templates.intelblas_gemm('T','N')]
res += [sc.templates.intelblas_gemm_image('T','N')]
elif template is sc.templates.gemm_nt:
res += [sc.templates.cublas_gemm('N','T')]
if 'float32' in dtype.__name__:
if 'float64' not in dtype.__name__:
res += [sc.templates.intelblas_gemm('N','T')]
res += [sc.templates.intelblas_gemm_image('N','T')]
elif template is sc.templates.gemm_tt:
res += [sc.templates.cublas_gemm('T','T')]
if 'float32' in dtype.__name__:
if 'float64' not in dtype.__name__:
res += [sc.templates.intelblas_gemm('T','T')]
res += [sc.templates.intelblas_gemm_image('T','T')]
return res
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12 changes: 7 additions & 5 deletions tune/android/tune/tune.py
Original file line number Diff line number Diff line change
Expand Up @@ -184,11 +184,13 @@ def save():
save()
#Adding external profiles
for prof in tools.external_profiles(operation):
profiles.append(prof.__class__.__name__)
for x, y in zip(X, Y):
tree, operands = tools.tree_of(operation, x, context)
perf = performance(x,tools.benchmark(prof, tree, operation))
y.append(perf)
profiles.append(prof.__class__.__name__)
for x, y in zip(X, Y):
tree, operands = tools.tree_of(operation, x, context)
perf = performance(x,tools.benchmark(prof, tree, operation))
if max(y) < perf:
print x, '\t', prof.__class__.__name__, '\toutperform: \t', int(perf), tools.metric_name_of(operation)
y.append(perf)
#Pruning of useless profiles
X = np.array(X)
Y = np.array(Y)
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