Thrust examples

• Recurrence_nonlinear_partial_differential_equations.cu: Evaluating recurrence relations involved in nonlinear partial differential equations;
• Thrust_Stream_Breadth_First.cu: Using CUDA streams with Thrust APIs;
• cumulative_distribution.cu: calculate the cumulative distribution of a probability distribution;
• min_element.cu: calculate the minimum element of an array along with its position;
• strided_reduction.cu: sum every STRIDE elements of a vector;
• Reduce_rows.cu: reduce each row of a matrix, see Reduce matrix rows with CUDA;
• Reduce_columns.cu: reduce each column of a matrix, see Reduce matrix columns with CUDA;
• Max_2_elements_of_each_row.cu: determine the maximum 2 elements of each row, see Determining the 2 largest elements and their positions in each matrix row with CUDA Thrust;
• Min_element_of_each_column.cu: determine the least element of each column, see Determining the least element and its position in each matrix column with CUDA Thrust;
• Sort_rows.cu: sort the rows of a matrix, see Concurrently sorting many arrays with CUDA Thrust;
• Sort_tuples.cu: sort tuples with a customized comparison operator, see Sort tuples with CUDA Thrust;
• Sort_by_key_with_tuple_key.cu: sort a vector by a tuple key with a customized comparison operator, see CUDA Thrust sort_by_key when the key is a tuple dealt with by zip_iterator's with custom comparison predicate;
• Reduce_by_key_with_tuple_key_host.cu: reduction by key with key which is a tuple, key and value arrays are host_vector's, see Reduction by key with tuple key;
• Reduce_by_key_with_tuple_key_device.cu: reduction by key with key which is a tuple, key and value arrays are see regular, cudaMalloc'ed vectors Reduction by key with tuple key;
• Row_reordering_by_key.cu: reordering the rows of a matrix by key, see Reordering matrix rows by key;
• Reduce_columns_by_key.cu: reducing the columns of a matrix by key, see ???;
• Find_key_occurrences_and_first_positions.cu: finding the position of the first occurrences of keys and counting the number of their occurrences, see Finding the number of occurrences of keys and the positions of first occurrences of keys by CUDA Thrust;
• Row_scaling.cu: scaling the rows of a matrix by a vector of coefficients, see Scaling the rows of a matrix with CUDA;
• Replicate_array_multiple_times.cu: replicate an array multiple times, see Replicate a vector multiple times using CUDA Thrust;
• Rowwise_Columnwise_operations_on_matrices.cu: apply the same operation on matrix rows or columns, see Row-wise/Column-wise operations on matrices with CUDA;
• Calculating_the_norm_of_arrays.cu: calculate the l^2 norm of an array, see Calculating the l2 norm of an array using CUDA Thrust;
• Calculating_the_projection_of_a_vector_on_a_set.cu: calculate the Euclidean distance between a vector and a set of vectors organized in a matrix, then selects the minimum, see ???;
• Calculating_Euclidean_distances_between_rows_of_two_matrices.cu: calculate the Euclidean distance between homologous rows of two matrices, see Computing the Euclidean distances between corresponding rows of matrices with CUDA;
• Find_minima_along_rows_along_with_their_column_indices.cu: minima of the rows of a matrix along with their column indices, see Find the minima of the columns of a matrix along with their corresponding row indices with CUDA Thrust;
• Find_minima_along_columns_along_with_their_row_indices.cu: minima of the columns of a matrix along with their row indices, see Find the minima of the rows of a matrix along with their corresponding column indices with CUDA Thrust;
• Thrust_inside_user_written_kernels.cu: using thrust::seq and thrust::device to call CUDA Thrust primitives from CUDA user written kernels, see Calling CUDA Thrust primitives from within a kernel;
• CostFunctionalCalculationThrust: using Thrust to calculate the cost functional for global optimization involving a large number of unknowns, see Cost functional calculation for global optimization in CUDA;
• ExponentialMovingAverageFilter.cu: using Thrust to implement an exponential moving average filter described by a difference equation, see Implementing an exponential moving average filter by CUDA Thrust;
• linspace.cu: Emulating Matlab’s linspace command by CUDA Thrust, see Emulating Matlab’s linspace command by CUDA Thrust;
• colon.cu: Emulating Matlab’s colon operator by CUDA Thrust, see Emulating Matlab’s colon operator by CUDA Thrust;
• SaxpyPlaceholders.cu: Using the placeholder technique to implement saxpy operation with CUDA Thrust, see CUDA Thrust shortcut math functions;
• SaxpyLambdas.cu: Using lambda expressions to implement saxpy operation with CUDA Thrust, see Lambda expressions with CUDA;
• Minmax_array.cu: Simultaneously finding the minimum and the maximum elements of an array with CUDA Thrust, see Returning the minimum and maximum elements of an array in CUDA;
• Sort_2_arrays_by_key.cu: Sorting two arrays by the same key - comparison between two approaches, see Sorting 3 arrays by key in CUDA (using Thrust perhaps);
• Sort_3_arrays_by_key.cu: Sorting three arrays by the same key - comparison between two approaches, see Sorting 3 arrays by key in CUDA (using Thrust perhaps);
• Count_occurrencies_in_an_array.cu: Count the occurrencies of individual elements in an array - comparison between two approaches, see Counting occurences of numbers in a CUDA array;