Merge pull request #19 from codereport/tinyapl

✨ bs for matx

Author: codereport

code/matx/CMakeLists.txt

@@ -11,7 +11,7 @@ FetchContent_Declare(matx
   GIT_TAG main
 )
 
-FetchContent_GetProperties(arrayfire)
+FetchContent_GetProperties(matx)
 if(NOT matx_POPULATED)
   FetchContent_Populate(matx)
   add_subdirectory(${matx_SOURCE_DIR} ${matx_BINARY_DIR} EXCLUDE_FROM_ALL)
@@ -27,8 +27,18 @@ if (NOT CMAKE_CXX_COMPILER)
   set(CMAKE_CXX_COMPILER "/usr/bin/g++")
 endif()
 
-set(CMAKE_CUDA_COMPILER /usr/local/cuda/bin/nvcc)
+set(CMAKE_CUDA_COMPILER /opt/nvidia/hpc_sdk/Linux_x86_64/24.11/compilers/bin/nvcc)
 
-add_executable(test test.cu)
+set(CMAKE_CUDA_ARCHITECTURES 75)
+
+# Optimization flags
+set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -O3 -ftree-vectorize -march=native")
+set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -O3 -use_fast_math -arch=compute_75 -code=sm_75")
+
+# Enable Link Time Optimization (LTO)4
+set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -flto")
+set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -flto")
+
+add_executable(test bs.cu)
 set_property(TARGET test PROPERTY CXX_STANDARD 20)
 target_link_libraries(test PRIVATE matx::matx)

code/matx/bs.cu

@@ -0,0 +1,138 @@
+#include "matx.h"
+#include <cassert>
+#include <cstdio>
+#include <math.h>
+#include <memory>
+
+using namespace matx;
+
+/**
+ * MatX uses C++ expression templates to build arithmetic expressions that compile into a lazily-evaluated
+ * type for executing on the device. Currently, nvcc cannot see certain optimizations
+ * when building the expression tree that would be obvious by looking at the code. Specifically any code reusing
+ * the same tensor multiple times appears to the compiler as separate tensors, and it may issue multiple load
+ * instructions. While caching helps, this can have a slight performance impact when compared to native CUDA
+ * kernels. To work around this problem, complex expressions can be placed in a custom operator by adding some
+ * boilerplate code around the original expression. This custom operator can then be used either alone or inside
+ * other arithmetic expressions, and only a single load is issues for each tensor.
+ *
+ * This example uses the Black-Scholes equtation to demonstrate the two ways to implement the equation in MatX, and
+ * shows the performance difference.
+ */
+
+/* Custom operator */
+template <class O, class I1, class I2, class I3, class I4, class I5>
+class BlackScholes : public BaseOp<BlackScholes<O, I1, I2, I3, I4, I5>>
+{
+private:
+    O out_;
+    I1 K_;
+    I2 V_;
+    I3 S_;
+    I4 r_;
+    I5 T_;
+
+public:
+    BlackScholes(O out, I1 K, I2 V, I3 S, I4 r, I5 T)
+        : out_(out), V_(V), S_(S), K_(K), r_(r), T_(T) {}
+
+    __device__ inline void operator()(index_t idx)
+    {
+        auto V = V_();
+        auto K = K_();
+        auto S = S_(idx);
+        auto T = T_();
+        auto r = r_();
+
+        auto VsqrtT = V * sqrt(T);
+        auto d1 = (log(S / K) + (r + 0.5f * V * V) * T) / VsqrtT;
+        auto d2 = d1 - VsqrtT;
+        auto cdf_d1 = normcdff(d1);
+        auto cdf_d2 = normcdff(d2);
+        auto expRT = exp(-1.f * r * T);
+
+        out_(idx) = S * cdf_d1 - K * expRT * cdf_d2;
+    }
+
+    __host__ __device__ inline index_t Size(uint32_t i) const { return out_.Size(i); }
+    static inline constexpr __host__ __device__ int32_t Rank() { return O::Rank(); }
+};
+
+template <typename T1>
+void compute_black_scholes_matx(tensor_t<T1, 0> &K,
+                                tensor_t<T1, 1> &S,
+                                tensor_t<T1, 0> &V,
+                                tensor_t<T1, 0> &r,
+                                tensor_t<T1, 0> &T,
+                                tensor_t<T1, 1> &output,
+                                cudaExecutor &exec)
+{
+    auto VsqrtT = V * sqrt(T);
+    auto d1 = (log(S / K) + (r + 0.5f * V * V) * T) / VsqrtT;
+    auto d2 = d1 - VsqrtT;
+    auto cdf_d1 = normcdf(d1);
+    auto cdf_d2 = normcdf(d2);
+    auto expRT = exp(-1.f * r * T);
+
+    (output = S * cdf_d1 - K * expRT * cdf_d2).run(exec);
+}
+
+int main([[maybe_unused]] int argc, [[maybe_unused]] char **argv)
+{
+    MATX_ENTER_HANDLER();
+
+    using dtype = float;
+
+    index_t input_size = 100000;
+    auto output_tensor = make_tensor<dtype>({input_size});
+    auto S_tensor = make_tensor<dtype>({input_size});
+    auto K_tensor = make_tensor<dtype>({});
+    auto V_tensor = make_tensor<dtype>({});
+    auto r_tensor = make_tensor<dtype>({});
+    auto T_tensor = make_tensor<dtype>({});
+    float time_ms;
+    int num_iterations = 99;
+
+    for (index_t i = 0; i < input_size; i++)
+    {
+        S_tensor(i) = (dtype)90 + dtype(i % 20);
+    }
+    K_tensor() = (dtype)100.;
+    V_tensor() = (dtype)0.1;
+    r_tensor() = (dtype)0.05;
+    T_tensor() = (dtype)1.0;
+
+    cudaStream_t stream;
+    cudaStreamCreate(&stream);
+    cudaExecutor exec{stream};
+    cudaEvent_t start, stop;
+    cudaEventCreate(&start);
+    cudaEventCreate(&stop);
+
+    BlackScholes(output_tensor, K_tensor, V_tensor, S_tensor, r_tensor, T_tensor).run(exec);
+    exec.sync();
+
+    cudaEventRecord(start, stream);
+    for (int i = 0; i < num_iterations; i++)
+    {
+        BlackScholes(output_tensor, K_tensor, V_tensor, S_tensor, r_tensor, T_tensor).run(exec);
+    }
+    cudaEventRecord(stop, stream);
+    exec.sync();
+    cudaEventElapsedTime(&time_ms, start, stop);
+
+    printf("Black-Scholes time = %.2fus per iteration\n",
+           time_ms * 1e3 / num_iterations);
+
+    compute_black_scholes_matx(K_tensor, S_tensor, V_tensor, r_tensor, T_tensor, output_tensor, exec);
+
+    printf("First 20 values of computed Black-Scholes output:\n");
+    for (index_t i = 0; i < 20; i++)
+    {
+        printf("%f\n", static_cast<float>(output_tensor(i)));
+    }
+
+    cudaStreamDestroy(stream);
+    MATX_CUDA_CHECK_LAST_ERROR();
+    MATX_EXIT_HANDLER();
+}