完成gf65521上的乘法

CMakeLists.txt

@@ -10,7 +10,7 @@ set(CMAKE_CUDA_ARCHITECTURES native)  # 设置CUDA架构
 
 # 设置编译选项
 set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -O3")
-set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -O3")
+set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -O3 -maxrregcount=128")
 
 project(cuElim_GF256 LANGUAGES CXX CUDA)  # 设置项目名和语言
 

benchmark/CMakeLists.txt

@@ -3,7 +3,8 @@ find_package(benchmark REQUIRED)
 include_directories(${PROJECT_SOURCE_DIR}/test)
 
 set(BENCH_SRC_FILES
-    "bench_mul8.cu"
+    "bench_gf256_mul.cu"
+    "bench_gfp_mul.cu"
 )
 
 foreach(SRC ${BENCH_SRC_FILES})

benchmark/bench_gf256_mul.cu

@@ -2,7 +2,7 @@
 #include "cuelim.cuh"
 
 template <MatGF256 (*GpuFunc)(const MatGF256 &, const MatGF256 &, const GF256 &)>
-void bench_mul8(benchmark::State &state)
+void bench_gf256_mul(benchmark::State &state)
 {
     GF256 ff(0b100011101);
     uint_fast32_t seed = 41921095;
@@ -16,4 +16,4 @@ void bench_mul8(benchmark::State &state)
     }
 }
 
-BENCHMARK_TEMPLATE(bench_mul8, gpu_mul)->Args({100000, 100000, 100000});
+BENCHMARK_TEMPLATE(bench_gf256_mul, gpu_mul)->Args({10000, 10000, 10000});

benchmark/bench_gfp_mul.cu

@@ -0,0 +1,18 @@
+#include <benchmark/benchmark.h>
+#include "test_header.cuh"
+
+static void bench_gfp(benchmark::State &state)
+{
+    uint_fast32_t seed = 41921095;
+    size_t x = state.range(0), y = state.range(1), z = state.range(2);
+    MatGFP A(x, y), B(y, z);
+    A.randomize(seed);
+    B.randomize(seed);
+    for (auto _ : state)
+    {
+        MatGFP C = A * B;
+    }
+}
+
+BENCHMARK(bench_gfp)->Args({10000, 10000, 10000});
+;

include/cuelim.cuh

@@ -4,4 +4,6 @@
 #include "gf256/gf256_mul.cuh"
 #include "gf256/gf256_elim.cuh"
 
+#include "gfp/gfp_mul.cuh"
+
 #endif

include/gf256/gf256_elim.cuh

@@ -19,7 +19,7 @@ void MatGF256::cpu_swap_row(size_t r1, size_t r2)
     }
 }
 
-size_t cpu_elim_base(base_t *base_col, base_t base_col_len, size_t st_r, size_t w, vector<size_t> &p_col, vector<size_t> &p_row, const GF256 &gf)
+size_t gf256_cpu_elim_base(base_t *base_col, base_t base_col_len, size_t st_r, size_t w, vector<size_t> &p_col, vector<size_t> &p_row, const GF256 &gf)
 {
     size_t rank = 0;
     size_t pivot[gf256_num];
@@ -58,7 +58,7 @@ size_t cpu_elim_base(base_t *base_col, base_t base_col_len, size_t st_r, size_t
     return rank;
 }
 
-__global__ void gpu_mksrc_kernel(base_t *src, size_t s_rowstride, base_t *spL, size_t src_rank, size_t width)
+__global__ void gf256_gpu_mksrc_kernel(base_t *src, size_t s_rowstride, base_t *spL, size_t src_rank, size_t width)
 {
     size_t w = blockIdx.x * blockDim.x + threadIdx.x;
     if (w >= width)
@@ -92,7 +92,7 @@ __global__ void gpu_mksrc_kernel(base_t *src, size_t s_rowstride, base_t *spL, s
     }
 }
 
-__global__ void gpu_elim_kernel(base_t *idx, base_t *tb, size_t tb_rowstride, base_t *data, size_t rowstride, size_t rank, base_t pivot_base, size_t st_skip, size_t width, size_t nrows)
+__global__ void gf256_gpu_elim_kernel(base_t *idx, base_t *tb, size_t tb_rowstride, base_t *data, size_t rowstride, size_t rank, base_t pivot_base, size_t st_skip, size_t width, size_t nrows)
 {
     size_t w = blockIdx.x * blockDim.x + threadIdx.x;
     size_t r = blockIdx.y * blockDim.y + threadIdx.y;
@@ -131,7 +131,7 @@ __host__ ElimResult MatGF256::gpu_elim(const GF256 &gf)
     {
         CUDA_CHECK(cudaMemcpy2D(base_col + rank, sizeof(base_t), at_base(rank, w), rowstride * sizeof(base_t), sizeof(base_t), nrows - rank, cudaMemcpyDefault));
 
-        size_t src_rank = cpu_elim_base(base_col + rank, nrows - rank, rank, w, p_col, p_row, gf);
+        size_t src_rank = gf256_cpu_elim_base(base_col + rank, nrows - rank, rank, w, p_col, p_row, gf);
 
         if (src_rank == 0)
         {
@@ -162,7 +162,7 @@ __host__ ElimResult MatGF256::gpu_elim(const GF256 &gf)
 
         dim3 block_src(THREAD_X);
         dim3 grid_src((width - w - 1) / block_src.x + 1);
-        gpu_mksrc_kernel<<<grid_src, block_src>>>(at_base(rank, w), rowstride, spL, src_rank, width);
+        gf256_gpu_mksrc_kernel<<<grid_src, block_src>>>(at_base(rank, w), rowstride, spL, src_rank, width);
         cudaDeviceSynchronize();
 
         dim3 block_tb(THREAD_X, THREAD_Y);
@@ -174,7 +174,7 @@ __host__ ElimResult MatGF256::gpu_elim(const GF256 &gf)
 
         dim3 block(THREAD_X, THREAD_Y);
         dim3 grid((width - w - 1) / block.x + 1, (nrows - 1) / block.y + 1);
-        gpu_elim_kernel<<<grid, block>>>(idx, tb.data, tb.rowstride, at_base(0, w), rowstride, src_rank, pivot_base, rank, width - w, nrows);
+        gf256_gpu_elim_kernel<<<grid, block>>>(idx, tb.data, tb.rowstride, at_base(0, w), rowstride, src_rank, pivot_base, rank, width - w, nrows);
         cudaDeviceSynchronize();
 
         rank += src_rank;

include/gf256/gf256_mul.cuh

@@ -3,7 +3,7 @@
 
 #include "gf256_mat.cuh"
 
-__global__ void gpu_addmul_kernel(base_t *a, size_t a_rowstride, base_t *tb, size_t tb_rowstride, base_t *c, size_t c_rowstride, size_t tb_num, size_t width, size_t nrows)
+__global__ void gf256_gpu_addmul_kernel(base_t *a, size_t a_rowstride, base_t *tb, size_t tb_rowstride, base_t *c, size_t c_rowstride, size_t tb_num, size_t width, size_t nrows)
 {
     size_t w = blockIdx.x * blockDim.x + threadIdx.x;
     size_t r = blockIdx.y * blockDim.y + threadIdx.y;
@@ -40,7 +40,7 @@ __host__ void MatGF256::gpu_addmul(const MatGF256 &a, const MatGF256 &b, const G
 
         dim3 block(THREAD_X, THREAD_Y);
         dim3 grid((b.width - 1) / block.x + 1, (nrows - 1) / block.y + 1);
-        gpu_addmul_kernel<<<grid, block>>>(a.at_base(0, w), a.rowstride, tb.data, tb.rowstride, data, rowstride, tb_num, width, nrows);
+        gf256_gpu_addmul_kernel<<<grid, block>>>(a.at_base(0, w), a.rowstride, tb.data, tb.rowstride, data, rowstride, tb_num, width, nrows);
         cudaDeviceSynchronize();
     }
 }

include/gfp/gfp_header.cuh

@@ -0,0 +1,27 @@
+#ifndef GFP_HEADER_CUH
+#define GFP_HEADER_CUH
+
+#include "../header.cuh"
+
+using gfp_t = uint32_t;
+#define gfp_bits 32
+
+static_assert(sizeof(gfp_t) * 8 == gfp_bits);
+
+static const gfp_t gfp = 65521;
+
+static const gfp_t gfp_zero = (gfp_t)0;
+static const gfp_t gfp_one = (gfp_t)1;
+static const gfp_t gfp_fullmask = (gfp_t)0xFF'FF;
+
+__managed__ gfp_t gfp_inv_table[gfp];
+
+void init_inv_table()
+{
+    gfp_inv_table[0] = 0;
+    gfp_inv_table[1] = 1;
+    for (int i = 2; i < gfp; ++i)
+        gfp_inv_table[i] = (gfp - gfp / i) * gfp_inv_table[gfp % i] % gfp;
+}
+
+#endif

include/gfp/gfp_mat.cuh

@@ -0,0 +1,242 @@
+#ifndef GFP_MAT_CUH
+#define GFP_MAT_CUH
+
+#include "gfp_header.cuh"
+
+#include <random>
+#include <vector>
+#include <algorithm>
+
+class MatGFP
+{
+public:
+    enum MatType
+    {
+        root,
+        window,
+        moved,
+    };
+    // 只能构造root矩阵
+    MatGFP(size_t nrows, size_t ncols) : nrows(nrows), ncols(ncols), type(root)
+    {
+        width = ncols;
+        rowstride = ((width - 1) / 8 + 1) * 8; // 以32字节（8*32bit）为单位对齐
+        CUDA_CHECK(cudaMallocManaged((void **)&data, nrows * rowstride * sizeof(gfp_t)));
+        CUDA_CHECK(cudaMemset(data, 0, nrows * rowstride * sizeof(gfp_t)));
+    }
+    // 只能以gfp_t为单位建立window矩阵
+    MatGFP(const MatGFP &src, size_t begin_ri, size_t begin_wi, size_t end_rj, size_t end_wj) : nrows(end_rj - begin_ri), ncols(end_wj - begin_wi), width(end_wj - begin_wi), rowstride(src.rowstride), type(window), data(src.at_base(begin_ri, begin_wi))
+    {
+        assert(begin_ri < end_rj && end_rj <= src.nrows && begin_wi < end_wj && end_wj <= src.width);
+    }
+    // 只能拷贝构造root矩阵
+    MatGFP(const MatGFP &m) : MatGFP(m.nrows, m.ncols)
+    {
+        CUDA_CHECK(cudaMemcpy2D(data, rowstride * sizeof(gfp_t), m.data, m.rowstride * sizeof(gfp_t), m.width * sizeof(gfp_t), nrows, cudaMemcpyDefault));
+    }
+    MatGFP(MatGFP &&m) noexcept : nrows(m.nrows), ncols(m.ncols), width(m.width), rowstride(m.rowstride), type(m.type), data(m.data)
+    {
+        m.type = moved;
+        m.data = nullptr;
+    }
+    MatGFP &operator=(const MatGFP &m)
+    {
+        if (this == &m)
+        {
+            return *this;
+        }
+        assert(nrows == m.nrows && ncols == m.ncols);
+        CUDA_CHECK(cudaMemcpy2D(data, rowstride * sizeof(gfp_t), m.data, m.rowstride * sizeof(gfp_t), m.width * sizeof(gfp_t), nrows, cudaMemcpyDefault));
+        return *this;
+    }
+    MatGFP &operator=(MatGFP &&m) noexcept
+    {
+        if (this == &m)
+        {
+            return *this;
+        }
+        if (type == root)
+        {
+            CUDA_CHECK(cudaFree(data));
+        }
+        nrows = m.nrows;
+        ncols = m.ncols;
+        width = m.width;
+        rowstride = m.rowstride;
+        type = m.type;
+        data = m.data;
+        m.type = moved;
+        m.data = nullptr;
+        return *this;
+    }
+
+    ~MatGFP()
+    {
+        if (type == root)
+        {
+            CUDA_CHECK(cudaFree(data));
+        }
+    }
+
+    inline gfp_t *at_base(size_t r, size_t w) const
+    {
+        return data + r * rowstride + w;
+    }
+
+    void randomize(uint_fast32_t seed)
+    {
+        assert(type == root);
+        static default_random_engine e(seed);
+        static uniform_int_distribution<gfp_t> d;
+        for (size_t r = 0; r < nrows; r++)
+        {
+            for (size_t w = 0; w < width; w++)
+            {
+                *at_base(r, w) = d(e) % gfp;
+            }
+        }
+    }
+
+    // 生成随机最简化行阶梯矩阵 前rank_col中选择nrows个主元列
+    void randomize(size_t rank_col, uint_fast32_t seed)
+    {
+        assert(nrows <= rank_col && rank_col <= ncols);
+        randomize(seed);
+        vector<size_t> pivot(rank_col);
+        iota(pivot.begin(), pivot.end(), 0);
+        random_shuffle(pivot.begin(), pivot.end());
+        pivot.resize(nrows);
+        sort(pivot.begin(), pivot.end());
+
+        vector<bool> pivotmask(width, true);
+        for (size_t r = 0; r < nrows; r++)
+        {
+            pivotmask[pivot[r]] = false;
+        }
+
+        for (size_t r = 0; r < nrows; r++)
+        {
+            for (size_t w = 0; w < pivot[r]; w++)
+            {
+                *at_base(r, w) = base_zero;
+            }
+            *at_base(r, pivot[r]) = base_one;
+            for (size_t w = pivot[r] + 1; w < rank_col; w++)
+            {
+                if (!pivotmask[w])
+                {
+                    *at_base(r, w) = base_zero;
+                }
+            }
+        }
+    }
+
+    bool operator==(const MatGFP &m) const
+    {
+        if (nrows != m.nrows || ncols != m.ncols)
+        {
+            return false;
+        }
+        for (size_t r = 0; r < nrows; r++)
+        {
+            for (size_t w = 0; w < width; w++)
+            {
+                if (*at_base(r, w) != *m.at_base(r, w))
+                {
+                    return false;
+                }
+            }
+        }
+        return true;
+    }
+
+    bool operator==(const gfp_t base) const
+    {
+        for (size_t r = 0; r < nrows; r++)
+        {
+            for (size_t w = 0; w < width; w++)
+            {
+                if (*at_base(r, w) != base)
+                {
+                    return false;
+                }
+            }
+        }
+        return true;
+    }
+    void operator+=(const MatGFP &m)
+    {
+        assert(nrows == m.nrows && ncols == m.ncols);
+        for (size_t r = 0; r < nrows; r++)
+        {
+            for (size_t w = 0; w < width; w++)
+            {
+                *at_base(r, w) = (*at_base(r, w) + *m.at_base(r, w)) % gfp;
+            }
+        }
+    }
+    MatGFP operator+(const MatGFP &m) const
+    {
+        MatGFP temp(*this);
+        temp += m;
+        return temp;
+    }
+    // a(m*k)*b(k,n) 其中k不超过65536
+    void cpu_addmul(const MatGFP &a, const MatGFP &b)
+    {
+        assert(a.ncols == b.nrows && a.nrows == nrows && b.ncols == ncols);
+        assert(a.ncols <= 65536);
+        for (size_t r = 0; r < nrows; r++)
+        {
+            for (size_t w = 0; w < width; w++)
+            {
+                for (size_t i = 0; i < a.ncols; i++)
+                {
+                    *at_base(r, w) += (*a.at_base(r, i) * *b.at_base(i, w)) % gfp;
+                }
+                *at_base(r, w) %= gfp;
+            }
+        }
+    }
+    void gpu_mul(const MatGFP &a, const MatGFP &b);
+
+    MatGFP operator*(const MatGFP &m) const
+    {
+        MatGFP temp(nrows, m.width);
+        temp.gpu_mul(*this, m);
+        return temp;
+    }
+
+    // void cpu_swap_row(size_t r1, size_t r2);
+    // ElimResult gpu_elim();
+
+    friend ostream &operator<<(ostream &out, const MatGFP &m);
+
+    size_t nrows, ncols, width;
+
+private:
+    MatGFP() : nrows(0), ncols(0), width(0), rowstride(0), type(moved), data(nullptr) {}
+
+    size_t rowstride;
+    MatType type;
+    gfp_t *data;
+};
+
+ostream &operator<<(ostream &out, const MatGFP &m)
+{
+    for (size_t r = 0; r < m.nrows; r++)
+    {
+        for (size_t w = 0; w < m.width; w++)
+        {
+#if gfp_bits == 64
+            printf("%05lu ", *m.at_base(r, w));
+#else
+            printf("%05u ", *m.at_base(r, w));
+#endif
+        }
+        printf("\n");
+    }
+    return out;
+}
+
+#endif

include/gfp/gfp_mul.cuh

@@ -0,0 +1,92 @@
+#ifndef GFP_MUL_CUH
+#define GFP_MUL_CUH
+
+#include "gfp_mat.cuh"
+
+static const int BlockRow = 128, BlockCol = 128; // 每个block处理c矩阵的一个子块
+static const int StepSize = 8;                   // block中一个循环处理的A矩阵的列数（B矩阵的行数）
+
+static_assert(BlockCol % THREAD_X == 0 && BlockRow % THREAD_Y == 0);
+
+__global__ void gfp_gpu_mul_kernel(gfp_t *__restrict__ a, const size_t a_rs, gfp_t *__restrict__ b, const size_t b_rs, gfp_t *__restrict__ c, const size_t c_rs, const size_t nrows, const size_t ncols, const size_t nsteps)
+{
+
+    const unsigned int bx = blockIdx.x;
+    const unsigned int by = blockIdx.y;
+    const unsigned int tx = threadIdx.x;
+    const unsigned int ty = threadIdx.y;
+    const unsigned int tid = ty * blockDim.x + tx;
+
+#if gfp_bits == 64
+    __shared__ alignas(8) gfp_t s_a[StepSize][BlockRow];
+    __shared__ alignas(8) gfp_t s_b[StepSize][BlockCol];
+#else
+    __shared__ gfp_t s_a[StepSize][BlockRow];
+    __shared__ gfp_t s_b[StepSize][BlockCol];
+#endif
+
+    gfp_t tmp_c[BlockRow / THREAD_Y][BlockCol / THREAD_X] = {0};
+
+    for (int s = 0; s < (nsteps - 1) / StepSize + 1; s++)
+    {
+        for (int k = tid; k < StepSize * BlockRow; k += blockDim.x * blockDim.y)
+        {
+            const int a_r = k / StepSize;
+            const int a_c = k % StepSize;
+            s_a[a_c][a_r] = by * BlockRow + a_r < nrows && s * StepSize + a_c < nsteps ? *at_base(a, a_rs, by * BlockRow + a_r, s * StepSize + a_c) : 0;
+            const int b_r = k / BlockCol;
+            const int b_c = k % BlockCol;
+            s_b[b_r][b_c] = s * StepSize + b_r < nsteps && bx * BlockCol + b_c < ncols ? *at_base(b, b_rs, s * StepSize + b_r, bx * BlockCol + b_c) : 0;
+        }
+        __syncthreads();
+        for (int k = 0; k < StepSize; k++)
+        {
+            for (int j = 0; j < BlockRow / THREAD_Y; j++)
+            {
+                for (int i = 0; i < BlockCol / THREAD_X; i++)
+                {
+#if gfp_bits == 64
+                    tmp_c[j][i] += (s_a[k][j * THREAD_Y + ty] * s_b[k][i * THREAD_X + tx]);
+#else
+                    tmp_c[j][i] += (s_a[k][j * THREAD_Y + ty] * s_b[k][i * THREAD_X + tx]) % gfp;
+#endif
+                }
+            }
+        }
+        __syncthreads();
+#if gfp_bits != 64
+        if (s & gfp_fullmask == gfp_fullmask)
+        {
+            for (int j = 0; j < BlockRow / THREAD_Y; j++)
+            {
+                for (int i = 0; i < BlockCol / THREAD_X; i++)
+                {
+                    tmp_c[j][i] %= gfp;
+                }
+            }
+        }
+#endif
+    }
+    for (int j = 0; j < BlockRow / THREAD_Y; j++)
+    {
+        for (int i = 0; i < BlockCol / THREAD_X; i++)
+        {
+            if (by * BlockRow + j * THREAD_Y + ty < nrows && bx * BlockCol + i * THREAD_X + tx < ncols)
+            {
+                *at_base(c, c_rs, by * BlockRow + j * THREAD_Y + ty, bx * BlockCol + i * THREAD_X + tx) = tmp_c[j][i] % gfp;
+            }
+        }
+    }
+}
+
+__host__ void MatGFP::gpu_mul(const MatGFP &a, const MatGFP &b)
+{
+    assert(a.ncols == b.nrows && a.nrows == nrows && b.ncols == ncols);
+
+    dim3 block(THREAD_X, THREAD_Y);
+    dim3 grid((width - 1) / block.x + 1, (nrows - 1) / block.y + 1);
+    gfp_gpu_mul_kernel<<<grid, block>>>(a.data, a.rowstride, b.data, b.rowstride, data, rowstride, nrows, width, a.width);
+    cudaDeviceSynchronize();
+}
+
+#endif

include/header.cuh

@@ -28,7 +28,13 @@ static const base_t base_fullmask = (base_t)0xFF'FF'FF'FF'FF'FF'FF'FF;
 static const size_t THREAD_X = 32; // 列
 static const size_t THREAD_Y = 8;  // 行
 
-__host__ __device__ base_t *at_base(base_t *base, size_t rowstride, size_t r, size_t w)
+// __host__ __device__ base_t *at_base(base_t *base, size_t rowstride, size_t r, size_t w)
+// {
+//     return base + r * rowstride + w;
+// }
+
+template <typename T>
+__host__ __device__ T *at_base(T *base, size_t rowstride, size_t r, size_t w)
 {
     return base + r * rowstride + w;
 }

src/main.cu

@@ -4,23 +4,16 @@
 
 #undef SHOW_PROGRESS_BAR
 
-bool test_elim(size_t rank, size_t rank_col, size_t nrows, size_t ncols, const GF256 &gf256, uint_fast32_t seed)
-{
-    assert(rank <= nrows && rank <= rank_col && rank_col <= ncols);
-    MatGF256 rdc(rank, ncols);
-    rdc.randomize(rank_col, seed);
-    MatGF256 mix(nrows, rank);
-    mix.randomize(seed);
-    MatGF256 src = gpu_mul(mix, rdc, gf256);
-    ElimResult res = src.gpu_elim(gf256);
-    MatGF256 win(src, 0, 0, res.rank, src.width);
-
-    return rdc == win;
-}
-
 int main()
 {
-    uint_fast32_t seed = 41921095;
-    GF256 gf256(0b100011101);
-    cout << test_elim(20000, 28000, 24000, 32000, gf256, seed) << endl;
+    int m = 1000, k = 1000, n = 1000;
+    MatGFP a(m, k);
+    MatGFP b(k, n);
+    MatGFP c(m, n);
+    a.randomize(10);
+    b.randomize(10);
+    c.cpu_addmul(a, b);
+    MatGFP d(m, n);
+    d.gpu_mul(a, b);
+    cout << (c == d) << endl;
 }

test/CMakeLists.txt

@@ -3,9 +3,10 @@ find_package(GTest REQUIRED)  # 查找GTest库
 include_directories(${PROJECT_SOURCE_DIR}/test)  # 添加测试头文件目录
 
 set(TEST_SRC_FILES
-    "test_gf256.cu"
-    "test_matrix.cu"
-    "test_elim.cu"
+    "test_gf256_header.cu"
+    "test_gf256_matrix.cu"
+    "test_gf256_elim.cu"
+    "test_gfp_mul.cu"
 )
 
 foreach(SRC ${TEST_SRC_FILES})

test/test_gf256_elim.cu

@@ -1,7 +1,7 @@
 #include <gtest/gtest.h>
 #include "test_header.cuh"
 
-bool test_elim(size_t rank, size_t rank_col, size_t nrows, size_t ncols, const GF256 &gf256, uint_fast32_t seed)
+bool test_gf256_elim(size_t rank, size_t rank_col, size_t nrows, size_t ncols, const GF256 &gf256, uint_fast32_t seed)
 {
     assert(rank <= nrows && rank <= rank_col && rank_col <= ncols);
     MatGF256 rdc(rank, ncols);
@@ -14,17 +14,17 @@ bool test_elim(size_t rank, size_t rank_col, size_t nrows, size_t ncols, const G
     return rdc == win;
 }
 
-TEST(TestElim, Small)
+TEST(TestGF256Elim, Small)
 {
     uint_fast32_t seed = 41921095;
     GF256 gf256(0b100011101);
-    EXPECT_TRUE(test_elim(5, 7, 6, 8, gf256, seed));
+    EXPECT_TRUE(test_gf256_elim(5, 7, 6, 8, gf256, seed));
 }
 
-TEST(TestElim, Mediem)
+TEST(TestGF256Elim, Mediem)
 {
     uint_fast32_t seed = 41921095;
     GF256 gf256(0b100011101);
-    EXPECT_TRUE(test_elim(50, 70, 60, 80, gf256, seed));
-    EXPECT_TRUE(test_elim(500, 700, 600, 800, gf256, seed));
+    EXPECT_TRUE(test_gf256_elim(50, 70, 60, 80, gf256, seed));
+    EXPECT_TRUE(test_gf256_elim(500, 700, 600, 800, gf256, seed));
 }

test/test_gf256_header.cu

@@ -19,7 +19,7 @@ vector<gf256_t> expect_inv_table{
     0x14, 0x3F, 0xE6, 0xF0, 0x86, 0xB1, 0xE2, 0xF1, 0xFA, 0x74, 0xF3, 0xB4, 0x6D, 0x21, 0xB2, 0x6A,
     0xE3, 0xE7, 0xB5, 0xEA, 0x03, 0x8F, 0xD3, 0xC9, 0x42, 0xD4, 0xE8, 0x75, 0x7F, 0xFF, 0x7E, 0xFD};
 
-TEST(TestGF256, Inv)
+TEST(TestGF256Header, Inv)
 {
     GF256 gf256(0b100011101);
     for (size_t x = 0; x < 1 << gf256_len; x++)

test/test_gf256_matrix.cu

@@ -1,7 +1,7 @@
 #include <gtest/gtest.h>
 #include "test_header.cuh"
 
-TEST(TestMatrix, Equal)
+TEST(TestGF256Matrix, Equal)
 {
     MatGF256 a(50, 50);
     EXPECT_TRUE(a == base_zero);
@@ -15,7 +15,7 @@ TEST(TestMatrix, Equal)
     EXPECT_FALSE(a == b);
 }
 
-TEST(TestMatrix, Xor)
+TEST(TestGF256Matrix, Xor)
 {
     MatGF256 a(50, 50), b(50, 50);
     a.randomize(1243);

test/test_gfp_mul.cu

@@ -0,0 +1,27 @@
+#include <gtest/gtest.h>
+#include "test_header.cuh"
+
+bool test_gfp_mul(size_t m, size_t k, size_t n, uint_fast32_t seed)
+{
+    MatGFP a(m, k);
+    MatGFP b(k, n);
+    MatGFP c(m, n);
+    a.randomize(seed);
+    b.randomize(seed);
+    c.cpu_addmul(a, b);
+    MatGFP d = a * b;
+    return c == d;
+}
+
+TEST(TestGFPMul, Small)
+{
+    uint_fast32_t seed = 41921095;
+    EXPECT_TRUE(test_gfp_mul(5, 7, 6, seed));
+}
+
+TEST(TestGFPMul, Mediem)
+{
+    uint_fast32_t seed = 41921095;
+    EXPECT_TRUE(test_gfp_mul(50, 70, 60, seed));
+    EXPECT_TRUE(test_gfp_mul(500, 700, 600, seed));
+}