add profiling; add ce accurarcy; several other changes

8 files changed, 310 insertions, 25 deletions

diff --git a/matrix/cuda_helper.h b/matrix/cuda_helper.h
index cedc643..5e5f2ad 100644
--- a/matrix/cuda_helper.h
+++ b/matrix/cuda_helper.h
@@ -1,17 +1,23 @@
 #ifndef NERV_CUDA_HELPER_H
 #define NERV_CUDA_HELPER_H
+#include "cuda.h"
+#include "cuda_runtime.h"
+#include "driver_types.h"
+#include "cublas_v2.h"
 #define CUBLAS_SAFE_CALL(call) \
     do { \
         cublasStatus_t  err = (call); \
         if (err != CUBLAS_STATUS_SUCCESS) \
-            nerv_error(L, "cumatrix cublas error: %s", cublasGetErrorString(err)); \
+            nerv_error(L, "cumatrix cublas error: %s at %s:%d", \
+                        cublasGetErrorString(err), __FILE__, __LINE__); \
     } while (0)
 
 #define CUDA_SAFE_CALL(call) \
     do { \
         cudaError_t err = (call); \
         if (err != cudaSuccess) \
-            nerv_error(L, "cumatrix CUDA error: %s", cudaGetErrorString(err)); \
+            nerv_error(L, "cumatrix CUDA error: %s at %s:%d", \
+                            cudaGetErrorString(err), __FILE__, __LINE__); \
     } while (0)
 
 #define CUDA_SAFE_SYNC_CALL(call) \
@@ -52,4 +58,20 @@ static const char *cublasGetErrorString(cublasStatus_t err) {
     }
     return "<unknown>";
 }
+
+#define PROFILE_START \
+    do { \
+        cudaEvent_t start, stop; \
+        cudaEventCreate(&start); \
+        cudaEventCreate(&stop); \
+        cudaEventRecord(start, 0);
+#define PROFILE_STOP \
+        cudaEventRecord(stop, 0); \
+        cudaEventSynchronize(stop); \
+        float milliseconds = 0; \
+        cudaEventElapsedTime(&milliseconds, start, stop); \
+        accu_profile(__func__, milliseconds / 1000); \
+    } while (0);
+
+#define PROFILE_END 
 #endif
diff --git a/matrix/cukernel.h b/matrix/cukernel.h
index 7d2168e..23398c8 100644
--- a/matrix/cukernel.h
+++ b/matrix/cukernel.h
@@ -5,7 +5,9 @@ void cudak_(cuda_sigmoid)(const Matrix *a, Matrix *b);
 void cudak_(cuda_sigmoid_grad)(const Matrix *output, const Matrix *err, Matrix *nerr);
 void cudak_(cuda_rowsum)(const Matrix *a, Matrix *b);
 void cudak_(cuda_rowmax)(const Matrix *a, Matrix *b);
+void cudak_(cuda_rowmax_idx)(const Matrix *a, Matrix *b, Matrix *idx);
 void cudak_(cuda_colsum)(const Matrix *a, Matrix *b);
+void cudak_(cuda_colsame)(const Matrix *a, const Matrix *ref, Matrix *b);
 void cudak_(cuda_softmax_denominator)(const Matrix *a, const Matrix *max, Matrix *b);
 void cudak_(cuda_softmax_final)(const Matrix *a, const Matrix *max, const Matrix *deno, Matrix *b);
 void cudak_(cuda_add_row)(const Matrix *a, Matrix *b, double beta);
diff --git a/matrix/cumatrix.c b/matrix/cumatrix.c
index 51a3681..4ebc5ff 100644
--- a/matrix/cumatrix.c
+++ b/matrix/cumatrix.c
@@ -1,4 +1,38 @@
 #define NERV_GENERIC_CUMATRIX
+#include "../common.h"
+#include "cuda_helper.h"
+static cublasHandle_t cublas_handle;
+static HashMap *profile;
+
+int print_profile(lua_State *L) {
+    size_t i;
+    fprintf(stderr, "*** [nerv cumatrix profile] **\n");
+    for (i = 0; i < profile->size; i++)
+    {
+        HashNode *ptr;
+        for (ptr = profile->bucket[i]; ptr; ptr = ptr->next)
+        {
+            fprintf(stderr, "%s:\t%.6f\n", ptr->key, *(float *)ptr->val);
+        }
+    }
+    return 0;
+}
+
+int clear_profile(lua_State *L) {
+    hashmap_clear(profile);
+    return 0;
+}
+
+void accu_profile(const char *name, float delta) {
+    float *val = hashmap_getval(profile, name);
+    if (!val)
+    {
+        val = malloc(sizeof(float));
+        *val = 0;
+        hashmap_setval(profile, name, val);
+    }
+    *val += delta;
+}
 
 #define MATRIX_USE_FLOAT
 #define cuda_matrix_(NAME) cuda_matrix_float_##NAME
diff --git a/matrix/generic/cukernel.cu b/matrix/generic/cukernel.cu
index 05a1e78..fdab356 100644
--- a/matrix/generic/cukernel.cu
+++ b/matrix/generic/cukernel.cu
@@ -3,6 +3,7 @@
 #include <stdio.h>
 #include "matrix.h"
 #include "cuda.h"
+#include "float.h"
 #define CUDA_THREADS_N 16
 #define CUDA_THREADS_NN ((CUDA_THREADS_N) * (CUDA_THREADS_N))
 #define CEIL_DIV(a, b) (((a) + (b) - 1) / (b))
@@ -11,9 +12,12 @@ __global__ void cudak_(log_elem)(const MATRIX_ELEM *a, MATRIX_ELEM *b,
     int j = blockIdx.x * blockDim.x + threadIdx.x;
     int i = blockIdx.y * blockDim.y + threadIdx.y;
     long idx;
+    MATRIX_ELEM tmp;
     if (i >= nrow || j >= ncol) return;
     idx = j + i * stride;
-    b[idx] = log(a[idx]);
+    tmp = a[idx];
+    if(tmp < FLT_MIN) tmp = FLT_MIN;
+    b[idx] = log(tmp);
 }
 
 __global__ void cudak_(mul_elem)(const MATRIX_ELEM *a, const MATRIX_ELEM *b,
@@ -61,9 +65,9 @@ __global__ void cudak_(softmax_final)(const MATRIX_ELEM *a, MATRIX_ELEM *b,
 }
 
 __global__ void cudak_(block_reduce_rowsum)(const MATRIX_ELEM *input,
-                                MATRIX_ELEM *output,
-                                const int istride, const int ostride,
-                                const int n) {
+                                            MATRIX_ELEM *output,
+                                            const int istride, const int ostride,
+                                            const int n) {
     extern __shared__ MATRIX_ELEM cudak_(arr)[];
     int j = blockIdx.x * blockDim.x + threadIdx.x;
     cudak_(arr)[threadIdx.x] = j < n ? input[j + istride * blockIdx.y] : 0;
@@ -96,6 +100,26 @@ __global__ void cudak_(block_reduce_colsum)(const MATRIX_ELEM *input,
         output[blockIdx.x + ostride * blockIdx.y] = cudak_(arr)[0];
 }
 
+__global__ void cudak_(block_reduce_colsame)(const MATRIX_ELEM *input,
+                                            const MATRIX_ELEM *ref_input,
+                                            MATRIX_ELEM *output,
+                                            const int istride, const int ostride,
+                                            const int n) {
+    extern __shared__ MATRIX_ELEM cudak_(arr)[];
+    int i = blockIdx.y * blockDim.y + threadIdx.y;
+    cudak_(arr)[threadIdx.y] = (i < n && input[blockIdx.x + istride * i] == \
+                                        ref_input[blockIdx.x + istride * i]) ? 1.0 : 0;
+    __syncthreads();
+    for (int offset = blockDim.y >> 1;  offset; offset >>= 1)
+    {
+        if (threadIdx.y < offset)
+            cudak_(arr)[threadIdx.y] += cudak_(arr)[threadIdx.y + offset];
+        __syncthreads();
+    }
+    if (threadIdx.y == 0)
+        output[blockIdx.x + ostride * blockIdx.y] = cudak_(arr)[0];
+}
+
 __global__ void cudak_(block_reduce_softmax_rowsum)(const MATRIX_ELEM *input,
                                         MATRIX_ELEM *output,
                                         const MATRIX_ELEM *max,
@@ -117,9 +141,9 @@ __global__ void cudak_(block_reduce_softmax_rowsum)(const MATRIX_ELEM *input,
 }
 
 __global__ void cudak_(block_reduce_rowmax)(const MATRIX_ELEM *input,
-                                MATRIX_ELEM *output,
-                                const int istride, const int ostride,
-                                const int n) {
+                                            MATRIX_ELEM *output,
+                                            const int istride, const int ostride,
+                                            const int n) {
     extern __shared__ MATRIX_ELEM cudak_(arr)[];
     int j = blockIdx.x * blockDim.x + threadIdx.x;
     cudak_(arr)[threadIdx.x] = j < n ? input[j + istride * blockIdx.y] : 0;
@@ -129,8 +153,9 @@ __global__ void cudak_(block_reduce_rowmax)(const MATRIX_ELEM *input,
         if (threadIdx.x < offset)
         {
             MATRIX_ELEM l = cudak_(arr)[threadIdx.x],
-                  r = cudak_(arr)[threadIdx.x + offset];
-            if (r > l) cudak_(arr)[threadIdx.x] = r;
+                        r = cudak_(arr)[threadIdx.x + offset];
+            if (r > l)
+                cudak_(arr)[threadIdx.x] = r;
         }
         __syncthreads();
     }
@@ -138,6 +163,40 @@ __global__ void cudak_(block_reduce_rowmax)(const MATRIX_ELEM *input,
         output[blockIdx.x + ostride * blockIdx.y] = cudak_(arr)[0];
 }
 
+__global__ void cudak_(block_reduce_rowmax_idx)(const MATRIX_ELEM *input,
+                                                const MATRIX_ELEM *idx_input,
+                                                MATRIX_ELEM *output,
+                                                MATRIX_ELEM *idx_output,
+                                                const int istride, const int ostride,
+                                                const int n) {
+    extern __shared__ MATRIX_ELEM cudak_(arr)[];
+    MATRIX_ELEM *arr_val = cudak_(arr);
+    MATRIX_ELEM *arr_idx = arr_val + blockDim.x;
+    int j = blockIdx.x * blockDim.x + threadIdx.x;
+    arr_val[threadIdx.x] = j < n ? input[j + istride * blockIdx.y] : 0;
+    arr_idx[threadIdx.x] = j < n ? idx_input[j + istride * blockIdx.y] : 0;
+    __syncthreads();
+    for (int offset = blockDim.x >> 1;  offset; offset >>= 1)
+    {
+        if (threadIdx.x < offset)
+        {
+            MATRIX_ELEM l = arr_val[threadIdx.x],
+                        r = arr_val[threadIdx.x + offset];
+            if (r > l)
+            {
+                arr_val[threadIdx.x] = r;
+                arr_idx[threadIdx.x] = arr_idx[threadIdx.x + offset];
+            }
+        }
+        __syncthreads();
+    }
+    if (threadIdx.x == 0)
+    {
+        output[blockIdx.x + ostride * blockIdx.y] = arr_val[0];
+        idx_output[blockIdx.x + ostride * blockIdx.y] = arr_idx[0];
+    }
+}
+
 __global__ void cudak_(add_row)(const MATRIX_ELEM *a, MATRIX_ELEM *b,
                                 int nrow, int ncol, int stride, double beta) {
     int j = blockIdx.x * blockDim.x + threadIdx.x;
@@ -196,6 +255,14 @@ __global__ void cudak_(decompress)(const MATRIX_ELEM *a, MATRIX_ELEM *b,
     b[lrintf(a[j + i * stride_a]) + i * stride_b] = 1.0;
 }
 
+__global__ void cudak_(gen_col_idx)(MATRIX_ELEM *b,
+                                    int nrow, int ncol, int stride) {
+    int j = blockIdx.x * blockDim.x + threadIdx.x;
+    int i = blockIdx.y * blockDim.y + threadIdx.y;
+    if (i >= nrow || j >= ncol) return;
+    b[j + i * stride] = j;
+}
+
 extern "C" {
 #include "../cukernel.h"
     void cudak_(cuda_log_elem)(const Matrix *a, Matrix *b) {
@@ -261,10 +328,32 @@ extern "C" {
         cudaFree(res);
     }
 
+    void cudak_(cuda_colsame)(const Matrix *a, const Matrix *ref, Matrix *b) {
+        dim3 block(1, CUDA_THREADS_NN);
+        int nrow = a->nrow;
+        int blocks_per_col = CEIL_DIV(nrow, block.y);
+        dim3 grid(a->ncol, blocks_per_col);
+        MATRIX_ELEM *res;
+        size_t stride;
+        cudaMallocPitch(&res, &stride, a->ncol * sizeof(MATRIX_ELEM), blocks_per_col);
+        cudak_(block_reduce_colsame)<<<grid, block, block.y * sizeof(MATRIX_ELEM)>>> \
+            (MATRIX_ELEM_PTR(a), MATRIX_ELEM_PTR(ref), res,
+             a->stride / sizeof(MATRIX_ELEM), stride / sizeof(MATRIX_ELEM),
+             nrow);
+        nrow = blocks_per_col;
+        assert((unsigned long)nrow <= block.y);
+        grid.y = 1;
+        cudak_(block_reduce_colsum)<<<grid, block, block.y * sizeof(MATRIX_ELEM)>>> \
+            (res, MATRIX_ELEM_PTR(b),
+             stride / sizeof(MATRIX_ELEM), b->stride / sizeof(MATRIX_ELEM),
+             nrow);
+        cudaFree(res);
+    }
+
     void cudak_(cuda_colsum)(const Matrix *a, Matrix *b) {
         dim3 block(1, CUDA_THREADS_NN);
         int nrow = a->nrow;
-        int blocks_per_col = CEIL_DIV(nrow, block.x);
+        int blocks_per_col = CEIL_DIV(nrow, block.y);
         dim3 grid(a->ncol, blocks_per_col);
         MATRIX_ELEM *res;
         size_t stride;
@@ -344,6 +433,35 @@ extern "C" {
         cudaFree(res);
     }
 
+    void cudak_(cuda_rowmax_idx)(const Matrix *a, Matrix *b, Matrix *b_idx) {
+        dim3 block(CUDA_THREADS_NN, 1);
+        int ncol = a->ncol;
+        int blocks_per_row = CEIL_DIV(ncol, block.x);
+        dim3 grid(blocks_per_row, a->nrow);
+        MATRIX_ELEM *a_idx, *res, *res_idx;
+        size_t stride;
+        cudaMallocPitch(&a_idx, &stride, a->stride, a->nrow);
+        cudak_(gen_col_idx)<<<grid, block>>>(a_idx, a->nrow, ncol, stride / sizeof(MATRIX_ELEM));
+        cudaMallocPitch(&res, &stride, blocks_per_row * sizeof(MATRIX_ELEM), a->nrow);
+        cudaMallocPitch(&res_idx, &stride, blocks_per_row * sizeof(MATRIX_ELEM), a->nrow);
+        cudak_(block_reduce_rowmax_idx)<<<grid, block,
+                                        2 * block.x * sizeof(MATRIX_ELEM)>>> \
+            (MATRIX_ELEM_PTR(a), a_idx, res, res_idx,
+             a->stride / sizeof(MATRIX_ELEM), stride / sizeof(MATRIX_ELEM),
+             ncol);
+        cudaFree(a_idx);
+        ncol = blocks_per_row;
+        assert((unsigned long)ncol <= block.x);
+        grid.x = 1;
+        cudak_(block_reduce_rowmax_idx)<<<grid, block,
+                                        2 * block.x * sizeof(MATRIX_ELEM)>>> \
+            (res, res_idx, MATRIX_ELEM_PTR(b), MATRIX_ELEM_PTR(b_idx),
+             stride / sizeof(MATRIX_ELEM), b->stride / sizeof(MATRIX_ELEM),
+             ncol);
+        cudaFree(res);
+        cudaFree(res_idx);
+    }
+
     /* in-place calc */
     void cudak_(cuda_add_row)(const Matrix *a, Matrix *b, double beta) {
         dim3 threadsPerBlock(CUDA_THREADS_N, CUDA_THREADS_N);
diff --git a/matrix/generic/cumatrix.c b/matrix/generic/cumatrix.c
index 373fc42..8e7d34f 100644
--- a/matrix/generic/cumatrix.c
+++ b/matrix/generic/cumatrix.c
@@ -11,15 +11,11 @@
 #define MATRIX_BASE_TNAME nerv_matrix_cuda_tname
 #define NERV_GENERIC_MATRIX
 #define NERV_GENERIC_CUKERNEL
+#define PROFILE_HASHMAP_SIZE 123457
 #include "../../common.h"
 #include "../cukernel.h"
-#include "cuda.h"
-#include "cuda_runtime.h"
-#include "driver_types.h"
-#include "cublas_v2.h"
 #include "../cuda_helper.h"
-
-static cublasHandle_t cublas_handle;
+#include <string.h>
 
 Matrix *nerv_matrix_(new_)(lua_State *L, long nrow, long ncol);
 void nerv_matrix_(data_free)(lua_State *L, Matrix *self);
@@ -27,6 +23,7 @@ void nerv_matrix_(data_free)(lua_State *L, Matrix *self);
 static void nerv_matrix_(add_)(lua_State *L, const Matrix *a, const Matrix *b,
                                 const Matrix *c,
                                 MATRIX_ELEM alpha, MATRIX_ELEM beta) {
+    PROFILE_START
     CUBLAS_SAFE_CALL(
             NERV_CUBLAS_(geam)(cublas_handle, CUBLAS_OP_N, CUBLAS_OP_N,
                 a->ncol, a->nrow,
@@ -35,6 +32,7 @@ static void nerv_matrix_(add_)(lua_State *L, const Matrix *a, const Matrix *b,
                 &beta,
                 MATRIX_ELEM_PTR(b), b->stride / sizeof(MATRIX_ELEM),
                 MATRIX_ELEM_PTR(c), c->stride / sizeof(MATRIX_ELEM)));
+    PROFILE_STOP
 }
 
 static int nerv_matrix_(add)(lua_State *L) {
@@ -75,6 +73,7 @@ static int nerv_matrix_(mul)(lua_State *L) {
         nerv_error(L, "Wrong dimension of multipliers");
 /*    MATRIX_ELEM alpha = 1.0f, beta = 0.0f; */
     /* Because matrix in Nerv is row-major, here b comes first */
+    PROFILE_START
     CUBLAS_SAFE_CALL(
             NERV_CUBLAS_(gemm)(cublas_handle, tb, ta,
                 bn, am, bm,
@@ -83,6 +82,7 @@ static int nerv_matrix_(mul)(lua_State *L) {
                 MATRIX_ELEM_PTR(a), a->stride / sizeof(MATRIX_ELEM),
                 &beta,
                 MATRIX_ELEM_PTR(c), c->stride / sizeof(MATRIX_ELEM)));
+    PROFILE_STOP
     return 0;
 }
 
@@ -97,7 +97,9 @@ static int nerv_matrix_(sigmoid)(lua_State *L) {
     Matrix *a = luaT_checkudata(L, 1, nerv_matrix_(tname));
     Matrix *b = luaT_checkudata(L, 2, nerv_matrix_(tname));
     CHECK_SAME_DIMENSION(a, b);
+    PROFILE_START
     cudak_(cuda_sigmoid)(b, a);
+    PROFILE_STOP
     return 0;
 }
 
@@ -107,30 +109,38 @@ static int nerv_matrix_(sigmoid_grad)(lua_State *L) {
     Matrix *output = luaT_checkudata(L, 3, nerv_matrix_(tname));
     CHECK_SAME_DIMENSION(nerr, err);
     CHECK_SAME_DIMENSION(nerr, output);
+    PROFILE_START
     cudak_(cuda_sigmoid_grad)(output, err, nerr);
+    PROFILE_STOP
     return 0;
 }
 
 static int nerv_matrix_(softmax)(lua_State *L) {
     Matrix *a = luaT_checkudata(L, 2, nerv_matrix_(tname));
     Matrix *b = luaT_checkudata(L, 1, nerv_matrix_(tname));
-    Matrix *max;
+    Matrix *max, *max_idx;
     Matrix *dno;
     CHECK_SAME_DIMENSION(a, b);
     max = nerv_matrix_(new_)(L, a->nrow, 1);
+    max_idx = nerv_matrix_(new_)(L, a->nrow, 1);
     dno = nerv_matrix_(new_)(L, a->nrow, 1);
-    cudak_(cuda_rowmax)(a, max);
+    PROFILE_START
+    cudak_(cuda_rowmax_idx)(a, max, max_idx);
     cudak_(cuda_softmax_denominator)(a, max, dno);
     cudak_(cuda_softmax_final)(a, max, dno, b);
+    PROFILE_STOP
     nerv_matrix_(data_free)(L, max);
     nerv_matrix_(data_free)(L, dno);
-    return 0;
+    luaT_pushudata(L, max_idx, nerv_matrix_(tname));
+    return 1;
 }
 
 static int nerv_matrix_(rowsum)(lua_State *L) {
     Matrix *a = luaT_checkudata(L, 1, nerv_matrix_(tname));
     Matrix *b = nerv_matrix_(new_)(L, a->nrow, 1);
+    PROFILE_START
     cudak_(cuda_rowsum)(a, b);
+    PROFILE_STOP
     luaT_pushudata(L, b, nerv_matrix_(tname));
     return 1;
 }
@@ -138,7 +148,21 @@ static int nerv_matrix_(rowsum)(lua_State *L) {
 static int nerv_matrix_(colsum)(lua_State *L) {
     Matrix *a = luaT_checkudata(L, 1, nerv_matrix_(tname));
     Matrix *b = nerv_matrix_(new_)(L, 1, a->ncol);
+    PROFILE_START
     cudak_(cuda_colsum)(a, b);
+    PROFILE_STOP
+    luaT_pushudata(L, b, nerv_matrix_(tname));
+    return 1;
+}
+
+static int nerv_matrix_(colsame)(lua_State *L) {
+    Matrix *a = luaT_checkudata(L, 1, nerv_matrix_(tname));
+    Matrix *ref = luaT_checkudata(L, 2, nerv_matrix_(tname));
+    Matrix *b = nerv_matrix_(new_)(L, 1, a->ncol);
+    CHECK_SAME_DIMENSION(a, ref);
+    PROFILE_START
+    cudak_(cuda_colsame)(a, ref, b);
+    PROFILE_STOP
     luaT_pushudata(L, b, nerv_matrix_(tname));
     return 1;
 }
@@ -146,11 +170,24 @@ static int nerv_matrix_(colsum)(lua_State *L) {
 static int nerv_matrix_(rowmax)(lua_State *L) {
     Matrix *a = luaT_checkudata(L, 1, nerv_matrix_(tname));
     Matrix *b = nerv_matrix_(new_)(L, a->nrow, 1);
+    PROFILE_START
     cudak_(cuda_rowmax)(a, b);
+    PROFILE_STOP
     luaT_pushudata(L, b, nerv_matrix_(tname));
     return 1;
 }
 
+static int nerv_matrix_(rowmax_idx)(lua_State *L) {
+    Matrix *a = luaT_checkudata(L, 1, nerv_matrix_(tname));
+    Matrix *b = nerv_matrix_(new_)(L, a->nrow, 1);
+    Matrix *idx = nerv_matrix_(new_)(L, a->nrow, 1);
+    PROFILE_START
+    cudak_(cuda_rowmax_idx)(a, b, idx);
+    PROFILE_STOP
+    luaT_pushudata(L, b, nerv_matrix_(tname));
+    luaT_pushudata(L, idx, nerv_matrix_(tname));
+    return 2;
+}
 
 static int nerv_matrix_(add_row)(lua_State *L) {
     Matrix *a = luaT_checkudata(L, 2, nerv_matrix_(tname));
@@ -160,14 +197,18 @@ static int nerv_matrix_(add_row)(lua_State *L) {
         nerv_error(L, "the number of columns is not the same");
     if (a->nrow != 1)
         nerv_error(L, "a row vector is expected");
+    PROFILE_START
     cudak_(cuda_add_row)(a, b, beta);
+    PROFILE_STOP
     return 0;
 }
 
 static int nerv_matrix_(fill)(lua_State *L) {
     Matrix *self = luaT_checkudata(L, 1, nerv_matrix_(tname));
     double val = luaL_checknumber(L, 2);
+    PROFILE_START
     cudak_(cuda_fill)(self, val);
+    PROFILE_STOP
     return 0;
 }
 
@@ -183,11 +224,13 @@ static int nerv_matrix_(copy_fromd)(lua_State *L) {
         nerv_error(L, "invalid copy interval");
     if (a->ncol != b->ncol)
         nerv_error(L, "matrices should be of the same dimension");
+    PROFILE_START
     CUDA_SAFE_SYNC_CALL(
             cudaMemcpy2D(MATRIX_ROW_PTR(a, a_begin), a->stride,
                 MATRIX_ROW_PTR(b, b_begin), b->stride,
                 sizeof(MATRIX_ELEM) * b->ncol, b_end - b_begin,
                 cudaMemcpyDeviceToDevice));
+    PROFILE_STOP
     return 0;
 }
 
@@ -204,11 +247,13 @@ static int nerv_matrix_(copy_fromh)(lua_State *L) {
         nerv_error(L, "invalid copy interval");
     if (a->ncol != b->ncol)
         nerv_error(L, "matrices should be of the same dimension");
+    PROFILE_START
     CUDA_SAFE_SYNC_CALL(
             cudaMemcpy2D(MATRIX_ROW_PTR(a, a_begin), a->stride,
                 MATRIX_ROW_PTR(b, b_begin), b->stride,
                 sizeof(MATRIX_ELEM) * b->ncol, b_end - b_begin,
                 cudaMemcpyHostToDevice));
+    PROFILE_STOP
     return 0;
 }
 
@@ -224,11 +269,13 @@ static int nerv_matrix_(copy_toh)(lua_State *L) {
         nerv_error(L, "invalid copy interval");
     if (b->ncol != a->ncol)
         nerv_error(L, "matrices should be of the same dimension");
+    PROFILE_START
     CUDA_SAFE_SYNC_CALL(
             cudaMemcpy2D(MATRIX_ROW_PTR(b, b_begin), b->stride,
                 MATRIX_ROW_PTR(a, a_begin), a->stride,
                 sizeof(MATRIX_ELEM) * a->ncol, a_end - a_begin,
                 cudaMemcpyDeviceToHost));
+    PROFILE_STOP
     return 0;
 }
 
@@ -237,6 +284,7 @@ static int nerv_matrix_(trans)(lua_State *L) {
     Matrix *b = nerv_matrix_(new_)(L, a->ncol, a->nrow);
     MATRIX_ELEM alpha = 1, beta = 0;
     /* FIXME: possible memory leak when lua error is raised */
+    PROFILE_START
     CUBLAS_SAFE_CALL(
             NERV_CUBLAS_(geam)(cublas_handle, CUBLAS_OP_T, CUBLAS_OP_T,
                 a->nrow, a->ncol,
@@ -245,6 +293,7 @@ static int nerv_matrix_(trans)(lua_State *L) {
                 &beta,
                 MATRIX_ELEM_PTR(a), a->stride / sizeof(MATRIX_ELEM),
                 MATRIX_ELEM_PTR(b), b->stride / sizeof(MATRIX_ELEM)));
+    PROFILE_STOP
     luaT_pushudata(L, b, nerv_matrix_(tname));
     return 1;
 }
@@ -255,7 +304,9 @@ static int nerv_matrix_(mul_elem)(lua_State *L) {
     Matrix *c = luaT_checkudata(L, 1, nerv_matrix_(tname));
     CHECK_SAME_DIMENSION(a, b);
     CHECK_SAME_DIMENSION(a, c);
+    PROFILE_START
     cudak_(cuda_mul_elem)(a, b, c);
+    PROFILE_STOP
     return 0;
 }
 
@@ -263,7 +314,9 @@ static int nerv_matrix_(log_elem)(lua_State *L) {
     Matrix *a = luaT_checkudata(L, 2, nerv_matrix_(tname));
     Matrix *b = luaT_checkudata(L, 1, nerv_matrix_(tname));
     CHECK_SAME_DIMENSION(a, b);
+    PROFILE_START
     cudak_(cuda_log_elem)(a, b);
+    PROFILE_STOP
     return 0;
 }
 
@@ -274,8 +327,10 @@ static int nerv_matrix_(decompress)(lua_State *L) {
     if (a->ncol != 1)
         nerv_error(L, "the compressed matrix must be a column vector");
     b = nerv_matrix_(new_)(L, a->nrow, orig_col);
+    PROFILE_START
     cudak_(cuda_fill)(b, 0.0);
     cudak_(cuda_decompress)(a, b);
+    PROFILE_STOP
     luaT_pushudata(L, b, nerv_matrix_(tname));
     return 1;
 }
@@ -285,21 +340,25 @@ static int nerv_matrix_(copy_rows_fromh_by_idx)(lua_State *L) {
     Matrix *a = luaT_checkudata(L, 1, nerv_matrix_(tname));
     Matrix *b = luaT_checkudata(L, 2, MATRIX_CUMATRIX_HOST_TNAME);
     Matrix *idx = luaT_checkudata(L, 3, nerv_matrix_host_int_tname);
+    long nrow = a->nrow;
+    int b_begin = lua_gettop(L) > 3 ? luaL_checkinteger(L, 4) : 0;
+    if (!(0 <= b_begin && b_begin + nrow <= idx->ncol))
+        nerv_error(L, "invalid copy interval");
     long *idx_ptr = idx->data.i;
     int i;
-    long nrow = a->nrow;
     if (idx->nrow != 1)
         nerv_error(L, "index should be a vector");
-    if (idx->ncol != nrow)
-        nerv_error(L, "index dimension mismatch");
     if (a->ncol != b->ncol)
         nerv_error(L, "source/destination dimension mismatch");
     cudaStream_t *streams = (cudaStream_t*)malloc(sizeof(cudaStream_t) * nrow);
     for (i = 0; i < nrow; i++)
     {
+        int src_row = idx_ptr[b_begin + i];
+        if (!(0 <= src_row && src_row < b->nrow))
+            nerv_error(L, "invalid index");
         CUDA_SAFE_CALL(cudaStreamCreate(streams + i));
         CUDA_SAFE_CALL(cudaMemcpyAsync(MATRIX_ROW_PTR(a, i),
-                    MATRIX_ROW_PTR(b, idx_ptr[i]),
+                    MATRIX_ROW_PTR(b, src_row),
                     b->stride,
                     cudaMemcpyHostToDevice, streams[i]));
     }
@@ -308,6 +367,7 @@ static int nerv_matrix_(copy_rows_fromh_by_idx)(lua_State *L) {
         CUDA_SAFE_CALL(cudaStreamSynchronize(streams[i]));
         CUDA_SAFE_CALL(cudaStreamDestroy(streams[i]));
     }
+    free(streams);
     return 0;
 }
 
@@ -319,7 +379,9 @@ static int nerv_matrix_(expand_frm)(lua_State *L) {
         nerv_error(L, "mismatching number of frames");
     if (a->ncol != b->ncol * (context * 2 + 1))
         nerv_error(L, "the width should be 2 * context + 1");
+    PROFILE_START
     cudak_(cuda_expand_frm)(b, a, context);
+    PROFILE_STOP
     return 0;
 }
 
@@ -330,7 +392,9 @@ static int nerv_matrix_(rearrange_frm)(lua_State *L) {
     CHECK_SAME_DIMENSION(a, b);
     if (b->ncol % step)
         nerv_error(L, "the dimension of columns is not divisible by step");
+    PROFILE_START
     cudak_(cuda_rearrange_frm)(b, a, step);
+    PROFILE_STOP
     return 0;
 }
 
@@ -341,15 +405,19 @@ static int nerv_matrix_(scale_row)(lua_State *L) {
         nerv_error(L, "the number of columns is not the same");
     if (b->nrow != 1)
         nerv_error(L, "a row vector is expected");
+    PROFILE_START
     cudak_(cuda_scale_row)(b, a);
+    PROFILE_STOP
     return 0;
 }
 
 static const luaL_Reg nerv_matrix_(extra_methods)[] = {
     {"create", nerv_matrix_(create)},
     {"colsum", nerv_matrix_(colsum)},
+    {"colsame", nerv_matrix_(colsame)},
     {"rowsum", nerv_matrix_(rowsum)},
     {"rowmax", nerv_matrix_(rowmax)},
+    {"rowmax_idx", nerv_matrix_(rowmax_idx)},
     {"trans", nerv_matrix_(trans)},
     {"decompress", nerv_matrix_(decompress)},
     /* in-place calc */
@@ -375,6 +443,7 @@ static const luaL_Reg nerv_matrix_(extra_methods)[] = {
 static void cuda_matrix_(init)(lua_State *L) {
     luaN_append_methods(L, nerv_matrix_(extra_methods));
     cublasCreate(&cublas_handle);
+    profile = hashmap_create(PROFILE_HASHMAP_SIZE, bkdr_hash, strcmp);
 }
 
 static void cuda_matrix_(free)(lua_State *L, MATRIX_ELEM *ptr) {
@@ -383,7 +452,9 @@ static void cuda_matrix_(free)(lua_State *L, MATRIX_ELEM *ptr) {
 
 static void cuda_matrix_(alloc)(lua_State *L, MATRIX_ELEM **dptr,
                                 size_t *stride, long width, long height) {
+    PROFILE_START
     CUDA_SAFE_SYNC_CALL(cudaMallocPitch((void **)dptr, stride, width, height));
+    PROFILE_STOP
 }
 
 static MATRIX_ELEM cuda_matrix_(read)(lua_State *L, MATRIX_ELEM *data,
diff --git a/matrix/generic/mmatrix.c b/matrix/generic/mmatrix.c
index 4b722f3..75d1eb1 100644
--- a/matrix/generic/mmatrix.c
+++ b/matrix/generic/mmatrix.c
@@ -43,6 +43,9 @@ int nerv_matrix_(set_elem)(lua_State *L) {
 static const luaL_Reg nerv_matrix_(extra_methods)[];
 static void host_matrix_(init)(lua_State *L) {
     luaN_append_methods(L, nerv_matrix_(extra_methods));
+#ifdef MMATRIX_INIT
+    MMATRIX_INIT(L);
+#endif
 }
 
 #include "matrix.c"
@@ -114,6 +117,7 @@ static int nerv_matrix_(copy_from)(lua_State *L) {
             sizeof(MATRIX_ELEM) * b->ncol * (b_end - b_begin));
     return 0;
 }
+
 static const luaL_Reg nerv_matrix_(extra_methods)[] = {
     {"load", nerv_matrix_(load)},
     {"save", nerv_matrix_(save)},
diff --git a/matrix/init.c b/matrix/init.c
index b54cd12..7b7f478 100644
--- a/matrix/init.c
+++ b/matrix/init.c
@@ -9,12 +9,17 @@ void nerv_matrix_host_float_init(lua_State *L);
 void nerv_matrix_cuda_float_init(lua_State *L);
 void nerv_matrix_host_double_init(lua_State *L);
 void nerv_matrix_cuda_double_init(lua_State *L);
+void nerv_matrix_host_int_init(lua_State *L);
+int print_profile(lua_State *L);
+int clear_profile(lua_State *L);
 
 static const luaL_Reg matrix_methods[] = {
     {"__tostring__", nerv_error_method_not_implemented },
     {"__add__", nerv_error_method_not_implemented },
     {"__sub__", nerv_error_method_not_implemented },
     {"__mul__", nerv_error_method_not_implemented },
+    {"print_profile", print_profile},
+    {"clear_profile", clear_profile},
     {NULL, NULL}
 };
 
diff --git a/matrix/mmatrix.c b/matrix/mmatrix.c
index ab15197..81f8dfc 100644
--- a/matrix/mmatrix.c
+++ b/matrix/mmatrix.c
@@ -29,5 +29,34 @@ const char *nerv_matrix_(tname) = "nerv.MMatrixDouble";
 #define host_matrix_(NAME) host_matrix_int_##NAME
 #define nerv_matrix_(NAME) nerv_matrix_host_int_##NAME
 const char *nerv_matrix_(tname) = "nerv.MMatrixInt";
+#define MMATRIX_INIT(L) host_matrix_(init_extra)(L)
+
+static const luaL_Reg nerv_matrix_(extra_methods_int)[];
+static void host_matrix_(init_extra)(lua_State *L) {
+    luaN_append_methods(L, nerv_matrix_(extra_methods_int));
+}
+
 #include "generic/mmatrix.c"
 
+static int nerv_matrix_(perm_gen)(lua_State *L) {
+    int i, ncol = luaL_checkinteger(L, 1);
+    Matrix *self = nerv_matrix_(new_)(L, 1, ncol);
+    long *prow = self->data.i;
+    for (i = 0; i < ncol; i++)
+        prow[i] = i;
+    for (i = ncol - 1; i >= 0; i--)
+    {
+        size_t j = rand() % (i + 1);
+        long tmp = prow[i];
+        prow[i] = prow[j];
+        prow[j] = tmp;
+    }
+    luaT_pushudata(L, self, nerv_matrix_(tname));
+    return 1;
+}
+
+static const luaL_Reg nerv_matrix_(extra_methods_int)[] = {
+    {"perm_gen", nerv_matrix_(perm_gen)},
+    {NULL, NULL}
+};
+