summaryrefslogtreecommitdiff
path: root/src/tuning/routines/xgemm.cpp
blob: f45e8635f82727e7a4a6c2775a636a0cf2ed52fc (plain)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
// =================================================================================================
// This file is part of the CLBlast project. The project is licensed under Apache Version 2.0. This
// project loosely follows the Google C++ styleguide and uses a tab-size of two spaces and a max-
// width of 100 characters per line.
//
// Author(s):
//   Cedric Nugteren <www.cedricnugteren.nl>
//
// This file tunes the Xgemm routine at a high-level: choosing between the direct (single-kernel)
// and the in-direct (kernel plus pre/post-processing) methods.
//
// =================================================================================================

#include <exception>
#include <string>
#include <vector>
#include <assert.h>

#include "utilities/utilities.hpp"
#include "utilities/timing.hpp"

namespace clblast {
// =================================================================================================

template <typename T>
void RunGemmRoutine(const size_t value, const Queue& queue, const std::vector<Buffer<T>>& buffers) {
  auto queue_plain = queue();
  auto event = cl_event{};
  auto status = Gemm(Layout::kRowMajor, Transpose::kNo, Transpose::kNo,
                     value, value, value, ConstantOne<T>(),
                     buffers[0](), 0, value,
                     buffers[1](), 0, value, ConstantOne<T>(),
                     buffers[2](), 0, value,
                     &queue_plain, &event);
  if (status != StatusCode::kSuccess) {
    throw RuntimeError("Gemm failed with status " + ToString(status));
  }
  clWaitForEvents(1, &event);
  clReleaseEvent(event);
}

template <typename T>
void ForceSelectIndirectFrom(const size_t minimum_size, const Device &device) {
  const auto override_status = OverrideParameters(device(), "GemmRoutine", PrecisionValue<T>(),
                                                  {{"XGEMM_MIN_INDIRECT_SIZE", minimum_size}});
  if (override_status != StatusCode::kSuccess) {
    throw RuntimeError("OverrideParameters failed with status " + ToString(override_status));
  }
}

template <typename T>
void TuneXgemm(int argc, char* argv[]) {
  auto command_line_args = RetrieveCommandLineArguments(argc, argv);
  auto help = std::string{"* Options given/available:\n"};
  const auto platform_id = GetArgument(command_line_args, help, kArgPlatform, ConvertArgument(std::getenv("CLBLAST_PLATFORM"), size_t{0}));
  const auto device_id   = GetArgument(command_line_args, help, kArgDevice, ConvertArgument(std::getenv("CLBLAST_DEVICE"), size_t{0}));
  const auto precision   = GetArgument(command_line_args, help, kArgPrecision, Precision::kSingle);
  const auto num_runs    = GetArgument(command_line_args, help, kArgNumRuns, size_t{10});
  fprintf(stdout, "%s\n", help.c_str());

  // Values for m, n, and k
  const auto from = size_t{64};
  const auto to = size_t{2048};
  const auto step = size_t{64};

  // OpenCL initialisation
  const auto platform = Platform(platform_id);
  const auto device = Device(platform, device_id);
  if (!PrecisionSupported<T>(device)) {
    printf("* Unsupported precision, skipping this tuning run\n\n");
    return;
  }
  const auto context = Context(device);
  const auto queue = Queue(context, device);

  // Buffers
  auto a_mat = Buffer<T>(context, to * to);
  auto b_mat = Buffer<T>(context, to * to);
  auto c_mat = Buffer<T>(context, to * to);
  auto buffers = std::vector<Buffer<T>>{a_mat, b_mat, c_mat};

  // In-direct version
  printf("[----------] Testing the in-direct GEMM routine for m=n=k\n");
  ForceSelectIndirectFrom<T>(0, device);
  const auto indirect = TimeRoutine(from, to, step, num_runs, queue, buffers, RunGemmRoutine<T>);

  // Direct version
  printf("[----------] Testing the direct GEMM routine for m=n=k\n");
  ForceSelectIndirectFrom<T>(to * to * to + 1, device);
  const auto direct = TimeRoutine(from, to, step, num_runs, queue, buffers, RunGemmRoutine<T>);

  // Determining final score and best kernel selection point
  assert(indirect.size() == direct.size());
  printf("[----------] Collecting results\n");
  auto ratios = std::vector<double>(indirect.size());
  for (auto i = size_t{0}; i < indirect.size(); ++i) {
    ratios[i] = indirect[i].second / direct[i].second;
  }
  auto scores = std::vector<TuningResult>(ratios.size());
  for (auto i = size_t{0}; i < scores.size(); ++i) {
    auto score = 0;
    for (auto j = size_t{0}; j < i; ++j) { score += (ratios[j] <= 1.0); }
    for (auto j = i + 1; j < ratios.size(); ++j) { score += (ratios[j] > 1.0); }
    const auto epsilon = (scores.size() - i) / 1e3; // favour later results over earlier ones
    scores[i] = TuningResult{
        "gemm_kernel_selection",
        static_cast<double>(score) / static_cast<double>(scores.size() - 1) + epsilon,
        TuningParameters{
            TuningParameter{"XGEMM_MIN_INDIRECT_SIZE", indirect[i].first},
            TuningParameter{"PRECISION", static_cast<size_t>(precision)}
        }
    };
  }

  // Displaying results
  for (auto i = size_t{0}; i < indirect.size(); ++i) {
    assert(indirect[i].first == direct[i].first);
    const auto value = indirect[i].first;
    if (indirect[i].second != -1 && direct[i].second != -1) {
      const auto gflops_indirect = (2 * value * value * value) / (indirect[i].second * 1.0e6);
      const auto gflops_direct = (2 * value * value * value) / (direct[i].second * 1.0e6);
      printf("[ -------> ] %7zu %8.2lf %8.2lf %8.2lf\n",
             value, gflops_indirect, gflops_direct, scores[i].score);
    }
  }

  // Outputs the results as JSON to disk, including some meta-data
  const auto precision_string = std::to_string(static_cast<size_t>(precision));
  auto metadata = std::vector<std::pair<std::string,std::string>>{
      {"kernel_family", "gemm_routine"},
      {"arg_from", ToString(from)},
      {"arg_to", ToString(to)},
      {"arg_step", ToString(step)},
      {"precision", precision_string},
  };
  PrintTimingsToFileAsJSON("clblast_routine_gemm_" + precision_string + ".json",
                           device, platform, metadata, scores);

  printf("[  STATUS  ] All done\n");
}

// =================================================================================================
} // namespace clblast

// Shortcuts to the clblast namespace
using half = clblast::half;
using float2 = clblast::float2;
using double2 = clblast::double2;

// Main function (not within the clblast namespace)
int main(int argc, char *argv[]) {
  const auto command_line_args = clblast::RetrieveCommandLineArguments(argc, argv);
  switch(clblast::GetPrecision(command_line_args)) {
    case clblast::Precision::kHalf: clblast::TuneXgemm<half>(argc, argv); break;
    case clblast::Precision::kSingle: clblast::TuneXgemm<float>(argc, argv); break;
    case clblast::Precision::kDouble: clblast::TuneXgemm<double>(argc, argv); break;
    case clblast::Precision::kComplexSingle: clblast::TuneXgemm<float2>(argc, argv); break;
    case clblast::Precision::kComplexDouble: clblast::TuneXgemm<double2>(argc, argv); break;
  }
  return 0;
}

// =================================================================================================