cub::DeviceHistogram

Defined in /home/runner/work/cccl/cccl/cub/cub/device/device_histogram.cuh

struct DeviceHistogram

DeviceHistogram provides device-wide parallel operations for constructing histogram(s) from a sequence of samples data residing within device-accessible memory.

Overview

A histogram counts the number of observations that fall into each of the disjoint categories (known as bins).

Usage Considerations

Dynamic parallelism. DeviceHistogram methods can be called within kernel code on devices in which CUDA dynamic parallelism is supported.

Evenly-segmented bin ranges

template<typename SampleIteratorT, typename CounterT, typename LevelT, typename OffsetT> static inline cudaError_t HistogramEven(void *d_temp_storage, size_t &temp_storage_bytes, SampleIteratorT d_samples, CounterT *d_histogram, int num_levels, LevelT lower_level, LevelT upper_level, OffsetT num_samples, cudaStream_t stream = 0)

Computes an intensity histogram from a sequence of data samples using equal-width bins.

The number of histogram bins is (num_levels - 1)
All bins comprise the same width of sample values: (upper_level - lower_level) / (num_levels - 1).
If the common type of SampleT and LevelT is of integral type, the bin for a sample is computed as (sample - lower_level) * (num_levels - 1) / (upper_level - lower_level), round down to the nearest whole number. To protect against potential overflows, if the product (upper_level - lower_level) * (num_levels - 1) exceeds the number representable by an uint64_t, the cuda error cudaErrorInvalidValue is returned. If the common type is 128 bits wide, bin computation will use 128-bit arithmetic and cudaErrorInvalidValue will only be returned if bin computation would overflow for 128-bit arithmetic.
The ranges [d_samples, d_samples + num_samples) and [d_histogram, d_histogram + num_levels - 1) shall not overlap in any way.
cuda::std::common_type<LevelT, SampleT> must be valid, and both LevelT and SampleT must be valid arithmetic types. The common type must be convertible to int and trivially copyable.
When d_temp_storage is nullptr, no work is done and the required allocation size is returned in temp_storage_bytes.

Snippet

The code snippet below illustrates the computation of a six-bin histogram from a sequence of float samples

#include <cub/cub.cuh> // or equivalently <cub/device/device_histogram.cuh>

// Declare, allocate, and initialize device-accessible pointers for
// input samples and output histogram
int      num_samples;    // e.g., 10
float*   d_samples;      // e.g., [2.2, 6.1, 7.1, 2.9, 3.5, 0.3, 2.9, 2.1, 6.1, 999.5]
int*     d_histogram;    // e.g., [ -, -, -, -, -, -]
int      num_levels;     // e.g., 7       (seven level boundaries for six bins)
float    lower_level;    // e.g., 0.0     (lower sample value boundary of lowest bin)
float    upper_level;    // e.g., 12.0    (upper sample value boundary of upper bin)
...

// Determine temporary device storage requirements
void*    d_temp_storage = nullptr;
size_t   temp_storage_bytes = 0;
cub::DeviceHistogram::HistogramEven(
  d_temp_storage, temp_storage_bytes,
  d_samples, d_histogram, num_levels,
  lower_level, upper_level, num_samples);

// Allocate temporary storage
cudaMalloc(&d_temp_storage, temp_storage_bytes);

// Compute histograms
cub::DeviceHistogram::HistogramEven(
  d_temp_storage, temp_storage_bytes,
  d_samples, d_histogram, num_levels,
  lower_level, upper_level, num_samples);

// d_histogram   <-- [1, 5, 0, 3, 0, 0];

Template Parameters

SampleIteratorT – [inferred] Random-access input iterator type for reading input samples (may be a simple pointer type)
CounterT – [inferred] Integer type for histogram bin counters
LevelT – [inferred] Type for specifying boundaries (levels)
OffsetT – [inferred] Signed integer type for sequence offsets, list lengths, pointer differences, etc. (Consider using 32-bit values as offsets/lengths/etc. For example, int will typically yeild better performance than size_t in 64-bit memory mode.)

Parameters

d_temp_storage – [in] Device-accessible allocation of temporary storage. When nullptr, the required allocation size is written to temp_storage_bytes and no work is done.
temp_storage_bytes – [inout] Reference to size in bytes of d_temp_storage allocation
d_samples – [in] The pointer to the input sequence of data samples.
d_histogram – [out] The pointer to the histogram counter output array of length num_levels - 1.
num_levels – [in] The number of boundaries (levels) for delineating histogram samples. Implies that the number of bins is num_levels - 1.
lower_level – [in] The lower sample value bound (inclusive) for the lowest histogram bin.
upper_level – [in] The upper sample value bound (exclusive) for the highest histogram bin.
num_samples – [in] The number of input samples (i.e., the length of d_samples)
stream – [in]
[optional] CUDA stream to launch kernels within. Default is stream₀.

template<typename SampleIteratorT, typename CounterT, typename LevelT, typename OffsetT> static inline cudaError_t HistogramEven(void *d_temp_storage, size_t &temp_storage_bytes, SampleIteratorT d_samples, CounterT *d_histogram, int num_levels, LevelT lower_level, LevelT upper_level, OffsetT num_samples, cudaStream_t stream, bool debug_synchronous)

template<typename SampleIteratorT, typename CounterT, typename LevelT, typename OffsetT> static inline cudaError_t HistogramEven(void *d_temp_storage, size_t &temp_storage_bytes, SampleIteratorT d_samples, CounterT *d_histogram, int num_levels, LevelT lower_level, LevelT upper_level, OffsetT num_row_samples, OffsetT num_rows, size_t row_stride_bytes, cudaStream_t stream = 0)

Computes an intensity histogram from a sequence of data samples using equal-width bins.

A two-dimensional region of interest within d_samples can be specified using the num_row_samples, num_rows, and row_stride_bytes parameters.
The row stride must be a whole multiple of the sample data type size, i.e., (row_stride_bytes % sizeof(SampleT)) == 0.
The number of histogram bins is (num_levels - 1)
All bins comprise the same width of sample values: (upper_level - lower_level) / (num_levels - 1)
If the common type of SampleT and LevelT is of integral type, the bin for a sample is computed as (sample - lower_level) * (num_levels - 1) / (upper_level - lower_level), round down to the nearest whole number. To protect against potential overflows, if the product (upper_level - lower_level) * (num_levels - 1) exceeds the number representable by an uint64_t, the cuda error cudaErrorInvalidValue is returned. If the common type is 128 bits wide, bin computation will use 128-bit arithmetic and cudaErrorInvalidValue will only be returned if bin computation would overflow for 128-bit arithmetic.
For a given row r in [0, num_rows), let row_begin = d_samples + r * row_stride_bytes / sizeof(SampleT) and row_end = row_begin + num_row_samples. The ranges [row_begin, row_end) and [d_histogram, d_histogram + num_levels - 1) shall not overlap in any way.
cuda::std::common_type<LevelT, SampleT> must be valid, and both LevelT and SampleT must be valid arithmetic types. The common type must be convertible to int and trivially copyable.
When d_temp_storage is nullptr, no work is done and the required allocation size is returned in temp_storage_bytes.

Snippet

The code snippet below illustrates the computation of a six-bin histogram from a 2x5 region of interest within a flattened 2x7 array of float samples.

#include <cub/cub.cuh> // or equivalently <cub/device/device_histogram.cuh>

// Declare, allocate, and initialize device-accessible pointers for
// input samples and output histogram
int      num_row_samples;    // e.g., 5
int      num_rows;           // e.g., 2;
size_t   row_stride_bytes;   // e.g., 7 * sizeof(float)
float*   d_samples;          // e.g., [2.2, 6.1, 7.1, 2.9, 3.5,   -, -,
                             //        0.3, 2.9, 2.1, 6.1, 999.5, -, -]
int*     d_histogram;        // e.g., [ -, -, -, -, -, -]
int      num_levels;         // e.g., 7       (seven level boundaries for six bins)
float    lower_level;        // e.g., 0.0     (lower sample value boundary of lowest bin)
float    upper_level;        // e.g., 12.0    (upper sample value boundary of upper bin)
...

// Determine temporary device storage requirements
void*    d_temp_storage  = nullptr;
size_t   temp_storage_bytes = 0;
cub::DeviceHistogram::HistogramEven(
    d_temp_storage, temp_storage_bytes,
    d_samples, d_histogram, num_levels, lower_level, upper_level,
    num_row_samples, num_rows, row_stride_bytes);

// Allocate temporary storage
cudaMalloc(&d_temp_storage, temp_storage_bytes);

// Compute histograms
cub::DeviceHistogram::HistogramEven(
    d_temp_storage, temp_storage_bytes, d_samples, d_histogram,
    d_samples, d_histogram, num_levels, lower_level, upper_level,
    num_row_samples, num_rows, row_stride_bytes);

// d_histogram   <-- [1, 5, 0, 3, 0, 0];

Template Parameters

SampleIteratorT – [inferred] Random-access input iterator type for reading input samples. (may be a simple pointer type)
CounterT – [inferred] Integer type for histogram bin counters
LevelT – [inferred] Type for specifying boundaries (levels)
OffsetT – [inferred] Signed integer type for sequence offsets, list lengths, pointer differences, etc. (Consider using 32-bit values as offsets/lengths/etc. For example, int will typically yeild better performance than size_t in 64-bit memory mode.)

Parameters

d_temp_storage – [in] Device-accessible allocation of temporary storage. When nullptr, the required allocation size is written to temp_storage_bytes and no work is done.
temp_storage_bytes – [inout] Reference to size in bytes of d_temp_storage allocation
d_samples – [in] The pointer to the input sequence of data samples.
d_histogram – [out] The pointer to the histogram counter output array of length num_levels - 1.
num_levels – [in] The number of boundaries (levels) for delineating histogram samples. Implies that the number of bins is num_levels - 1.
lower_level – [in] The lower sample value bound (inclusive) for the lowest histogram bin.
upper_level – [in] The upper sample value bound (exclusive) for the highest histogram bin.
num_row_samples – [in] The number of data samples per row in the region of interest
num_rows – [in] The number of rows in the region of interest
row_stride_bytes – [in] The number of bytes between starts of consecutive rows in the region of interest
stream – [in]
[optional] CUDA stream to launch kernels within. Default is stream₀.

template<typename SampleIteratorT, typename CounterT, typename LevelT, typename OffsetT> static inline cudaError_t HistogramEven(void *d_temp_storage, size_t &temp_storage_bytes, SampleIteratorT d_samples, CounterT *d_histogram, int num_levels, LevelT lower_level, LevelT upper_level, OffsetT num_row_samples, OffsetT num_rows, size_t row_stride_bytes, cudaStream_t stream, bool debug_synchronous)

template<int NUM_CHANNELS, int NUM_ACTIVE_CHANNELS, typename SampleIteratorT, typename CounterT, typename LevelT, typename OffsetT> static inline cudaError_t MultiHistogramEven(void *d_temp_storage, size_t &temp_storage_bytes, SampleIteratorT d_samples, CounterT *d_histogram[NUM_ACTIVE_CHANNELS], const int num_levels[NUM_ACTIVE_CHANNELS], const LevelT lower_level[NUM_ACTIVE_CHANNELS], const LevelT upper_level[NUM_ACTIVE_CHANNELS], OffsetT num_pixels, cudaStream_t stream = 0)

Computes per-channel intensity histograms from a sequence of multi-channel “pixel” data samples using equal-width bins.

The input is a sequence of pixel structures, where each pixel comprises a record of NUM_CHANNELS consecutive data samples (e.g., an RGBA pixel).
NUM_CHANNELS can be up to 4.
Of the NUM_CHANNELS specified, the function will only compute histograms for the first NUM_ACTIVE_CHANNELS (e.g., only RGB histograms from RGBA pixel samples).
The number of histogram bins for channel_i is num_levels[i] - 1.
For channel_i, the range of values for all histogram bins have the same width: (upper_level[i] - lower_level[i]) / (num_levels[i] - 1)
If the common type of sample and level is of integral type, the bin for a sample is computed as (sample - lower_level[i]) * (num_levels - 1) / (upper_level[i] - lower_level[i]), round down to the nearest whole number. To protect against potential overflows, if, for any channel i, the product (upper_level[i] - lower_level[i]) * (num_levels[i] - 1) exceeds the number representable by an uint64_t, the cuda error cudaErrorInvalidValue is returned. If the common type is 128 bits wide, bin computation will use 128-bit arithmetic and cudaErrorInvalidValue will only be returned if bin computation would overflow for 128-bit arithmetic.
For a given channel c in [0, NUM_ACTIVE_CHANNELS), the ranges [d_samples, d_samples + NUM_CHANNELS * num_pixels) and [d_histogram[c], d_histogram[c] + num_levels[c] - 1) shall not overlap in any way.
cuda::std::common_type<LevelT, SampleT> must be valid, and both LevelT and SampleT must be valid arithmetic types. The common type must be convertible to int and trivially copyable.
When d_temp_storage is nullptr, no work is done and the required allocation size is returned in temp_storage_bytes.

Snippet

The code snippet below illustrates the computation of three 256-bin RGB histograms from a quad-channel sequence of RGBA pixels (8 bits per channel per pixel)

#include <cub/cub.cuh> // or equivalently <cub/device/device_histogram.cuh>

// Declare, allocate, and initialize device-accessible pointers for
// input samples and output histograms
int              num_pixels;         // e.g., 5
unsigned char*   d_samples;          // e.g., [(2, 6, 7, 5), (3, 0, 2, 1), (7, 0, 6, 2),
                                     //        (0, 6, 7, 5), (3, 0, 2, 6)]
int*             d_histogram[3];     // e.g., three device pointers to three device buffers,
                                     //       each allocated with 256 integer counters
int              num_levels[3];      // e.g., {257, 257, 257};
unsigned int     lower_level[3];     // e.g., {0, 0, 0};
unsigned int     upper_level[3];     // e.g., {256, 256, 256};
...

// Determine temporary device storage requirements
void*    d_temp_storage = nullptr;
size_t   temp_storage_bytes = 0;
cub::DeviceHistogram::MultiHistogramEven<4, 3>(
  d_temp_storage, temp_storage_bytes,
  d_samples, d_histogram, num_levels,
  lower_level, upper_level, num_pixels);

// Allocate temporary storage
cudaMalloc(&d_temp_storage, temp_storage_bytes);

// Compute histograms
cub::DeviceHistogram::MultiHistogramEven<4, 3>(
  d_temp_storage, temp_storage_bytes,
  d_samples, d_histogram, num_levels,
  lower_level, upper_level, num_pixels);

// d_histogram   <-- [ [1, 0, 1, 2, 0, 0, 0, 1, 0, 0, 0, ..., 0],
//                     [0, 3, 0, 0, 0, 0, 2, 0, 0, 0, 0, ..., 0],
//                     [0, 0, 2, 0, 0, 0, 1, 2, 0, 0, 0, ..., 0] ]

Template Parameters

NUM_CHANNELS – Number of channels interleaved in the input data (may be greater than the number of channels being actively histogrammed)
NUM_ACTIVE_CHANNELS – [inferred] Number of channels actively being histogrammed
SampleIteratorT – [inferred] Random-access input iterator type for reading input samples. (may be a simple pointer type)
CounterT – [inferred] Integer type for histogram bin counters
LevelT – [inferred] Type for specifying boundaries (levels)
OffsetT – [inferred] Signed integer type for sequence offsets, list lengths, pointer differences, etc. (Consider using 32-bit values as offsets/lengths/etc. For example, int will typically yeild better performance than size_t in 64-bit memory mode.)

Parameters

d_temp_storage – [in] Device-accessible allocation of temporary storage. When nullptr, the required allocation size is written to temp_storage_bytes and no work is done.
temp_storage_bytes – [inout] Reference to size in bytes of d_temp_storage allocation
d_samples – [in] The pointer to the multi-channel input sequence of data samples. The samples from different channels are assumed to be interleaved (e.g., an array of 32-bit pixels where each pixel consists of four RGBA 8-bit samples).
d_histogram – [out]
The pointers to the histogram counter output arrays, one for each active channel. For channel_i, the allocation length of d_histogram[i] should be num_levels[i] - 1`.
num_levels – [in]
The number of boundaries (levels) for delineating histogram samples in each active channel. Implies that the number of bins for channel_i is num_levels[i] - 1.
lower_level – [in] The lower sample value bound (inclusive) for the lowest histogram bin in each active channel.
upper_level – [in] The upper sample value bound (exclusive) for the highest histogram bin in each active channel.
num_pixels – [in] The number of multi-channel pixels (i.e., the length of d_samples / NUM_CHANNELS)
stream – [in]
[optional] CUDA stream to launch kernels within. Default is stream₀.

template<int NUM_CHANNELS, int NUM_ACTIVE_CHANNELS, typename SampleIteratorT, typename CounterT, typename LevelT, typename OffsetT> static inline cudaError_t MultiHistogramEven(void *d_temp_storage, size_t &temp_storage_bytes, SampleIteratorT d_samples, CounterT *d_histogram[NUM_ACTIVE_CHANNELS], const int num_levels[NUM_ACTIVE_CHANNELS], const LevelT lower_level[NUM_ACTIVE_CHANNELS], const LevelT upper_level[NUM_ACTIVE_CHANNELS], OffsetT num_pixels, cudaStream_t stream, bool debug_synchronous)

template<int NUM_CHANNELS, int NUM_ACTIVE_CHANNELS, typename SampleIteratorT, typename CounterT, typename LevelT, typename OffsetT> static inline cudaError_t MultiHistogramEven(void *d_temp_storage, size_t &temp_storage_bytes, SampleIteratorT d_samples, CounterT *d_histogram[NUM_ACTIVE_CHANNELS], const int num_levels[NUM_ACTIVE_CHANNELS], const LevelT lower_level[NUM_ACTIVE_CHANNELS], const LevelT upper_level[NUM_ACTIVE_CHANNELS], OffsetT num_row_pixels, OffsetT num_rows, size_t row_stride_bytes, cudaStream_t stream = 0)

Computes per-channel intensity histograms from a sequence of multi-channel “pixel” data samples using equal-width bins.

The input is a sequence of pixel structures, where each pixel comprises a record of NUM_CHANNELS consecutive data samples (e.g., an RGBA pixel).
NUM_CHANNELS can be up to 4.
Of the NUM_CHANNELS specified, the function will only compute histograms for the first NUM_ACTIVE_CHANNELS (e.g., only RGB histograms from RGBA pixel samples).
A two-dimensional region of interest within d_samples can be specified using the num_row_samples, num_rows, and row_stride_bytes parameters.
The row stride must be a whole multiple of the sample data type size, i.e., (row_stride_bytes % sizeof(SampleT)) == 0.
The number of histogram bins for channel_i is num_levels[i] - 1.
For channel_i, the range of values for all histogram bins have the same width: (upper_level[i] - lower_level[i]) / (num_levels[i] - 1)
If the common type of sample and level is of integral type, the bin for a sample is computed as (sample - lower_level[i]) * (num_levels - 1) / (upper_level[i] - lower_level[i]), round down to the nearest whole number. To protect against potential overflows, if, for any channel i, the product (upper_level[i] - lower_level[i]) * (num_levels[i] - 1) exceeds the number representable by an uint64_t, the cuda error cudaErrorInvalidValue is returned. If the common type is 128 bits wide, bin computation will use 128-bit arithmetic and cudaErrorInvalidValue will only be returned if bin computation would overflow for 128-bit arithmetic.
For a given row r in [0, num_rows), and sample s in [0, num_row_pixels), let row_begin = d_samples + r * row_stride_bytes / sizeof(SampleT), sample_begin = row_begin + s * NUM_CHANNELS, and sample_end = sample_begin + NUM_ACTIVE_CHANNELS. For a given channel c in [0, NUM_ACTIVE_CHANNELS), the ranges [sample_begin, sample_end) and [d_histogram[c], d_histogram[c] + num_levels[c] - 1) shall not overlap in any way.
cuda::std::common_type<LevelT, SampleT> must be valid, and both LevelT and SampleT must be valid arithmetic types. The common type must be convertible to int and trivially copyable.
When d_temp_storage is nullptr, no work is done and the required allocation size is returned in temp_storage_bytes.

Snippet

The code snippet below illustrates the computation of three 256-bin RGB histograms from a 2x3 region of interest of within a flattened 2x4 array of quad-channel RGBA pixels (8 bits per channel per pixel).

#include <cub/cub.cuh> // or equivalently <cub/device/device_histogram.cuh>

// Declare, allocate, and initialize device-accessible pointers for input
// samples and output histograms
int              num_row_pixels;     // e.g., 3
int              num_rows;           // e.g., 2
size_t           row_stride_bytes;   // e.g., 4 * sizeof(unsigned char) * NUM_CHANNELS
unsigned char*   d_samples;          // e.g., [(2, 6, 7, 5), (3, 0, 2, 1), (7, 0, 6, 2), (-, -, -, -),
                                     //        (0, 6, 7, 5), (3, 0, 2, 6), (1, 1, 1, 1), (-, -, -, -)]
int*             d_histogram[3];     // e.g., three device pointers to three device buffers,
                                     //       each allocated with 256 integer counters
int              num_levels[3];      // e.g., {257, 257, 257};
unsigned int     lower_level[3];     // e.g., {0, 0, 0};
unsigned int     upper_level[3];     // e.g., {256, 256, 256};
...

// Determine temporary device storage requirements
void*    d_temp_storage = nullptr;
size_t   temp_storage_bytes = 0;
cub::DeviceHistogram::MultiHistogramEven<4, 3>(
  d_temp_storage, temp_storage_bytes,
  d_samples, d_histogram, num_levels, lower_level, upper_level,
  num_row_pixels, num_rows, row_stride_bytes);

// Allocate temporary storage
cudaMalloc(&d_temp_storage, temp_storage_bytes);

// Compute histograms
cub::DeviceHistogram::MultiHistogramEven<4, 3>(
  d_temp_storage, temp_storage_bytes,
  d_samples, d_histogram, num_levels, lower_level, upper_level,
  num_row_pixels, num_rows, row_stride_bytes);

// d_histogram   <-- [ [1, 1, 1, 2, 0, 0, 0, 1, 0, 0, 0, ..., 0],
//                     [0, 4, 0, 0, 0, 0, 2, 0, 0, 0, 0, ..., 0],
//                     [0, 1, 2, 0, 0, 0, 1, 2, 0, 0, 0, ..., 0] ]

Template Parameters

NUM_CHANNELS – Number of channels interleaved in the input data (may be greater than the number of channels being actively histogrammed)
NUM_ACTIVE_CHANNELS – [inferred] Number of channels actively being histogrammed
SampleIteratorT – [inferred] Random-access input iterator type for reading input samples. (may be a simple pointer type)
CounterT – [inferred] Integer type for histogram bin counters
LevelT – [inferred] Type for specifying boundaries (levels)
OffsetT – [inferred] Signed integer type for sequence offsets, list lengths, pointer differences, etc. (Consider using 32-bit values as offsets/lengths/etc. For example, int will typically yeild better performance than size_t in 64-bit memory mode.)

Parameters

d_temp_storage – [in] Device-accessible allocation of temporary storage. When nullptr, the required allocation size is written to temp_storage_bytes and no work is done.
temp_storage_bytes – [inout] Reference to size in bytes of d_temp_storage allocation
d_samples – [in] The pointer to the multi-channel input sequence of data samples. The samples from different channels are assumed to be interleaved (e.g., an array of 32-bit pixels where each pixel consists of four RGBA 8-bit samples).
d_histogram – [out]
The pointers to the histogram counter output arrays, one for each active channel. For channel_i, the allocation length of d_histogram[i] should be num_levels[i] - 1.
num_levels – [in]
The number of boundaries (levels) for delineating histogram samples in each active channel. Implies that the number of bins for channel_i is num_levels[i] - 1.
lower_level – [in] The lower sample value bound (inclusive) for the lowest histogram bin in each active channel.
upper_level – [in] The upper sample value bound (exclusive) for the highest histogram bin in each active channel.
num_row_pixels – [in] The number of multi-channel pixels per row in the region of interest
num_rows – [in] The number of rows in the region of interest
row_stride_bytes – [in] The number of bytes between starts of consecutive rows in the region of interest
stream – [in]
[optional] CUDA stream to launch kernels within. Default is stream₀.

template<int NUM_CHANNELS, int NUM_ACTIVE_CHANNELS, typename SampleIteratorT, typename CounterT, typename LevelT, typename OffsetT> static inline cudaError_t MultiHistogramEven(void *d_temp_storage, size_t &temp_storage_bytes, SampleIteratorT d_samples, CounterT *d_histogram[NUM_ACTIVE_CHANNELS], const int num_levels[NUM_ACTIVE_CHANNELS], const LevelT lower_level[NUM_ACTIVE_CHANNELS], const LevelT upper_level[NUM_ACTIVE_CHANNELS], OffsetT num_row_pixels, OffsetT num_rows, size_t row_stride_bytes, cudaStream_t stream, bool debug_synchronous)

Custom bin ranges

template<typename SampleIteratorT, typename CounterT, typename LevelT, typename OffsetT> static inline cudaError_t HistogramRange(void *d_temp_storage, size_t &temp_storage_bytes, SampleIteratorT d_samples, CounterT *d_histogram, int num_levels, const LevelT *d_levels, OffsetT num_samples, cudaStream_t stream = 0)

Computes an intensity histogram from a sequence of data samples using the specified bin boundary levels.

The number of histogram bins is (num_levels - 1)
The value range for bin_i is [level[i], level[i+1])
The range [d_histogram, d_histogram + num_levels - 1) shall not overlap [d_samples, d_samples + num_samples) nor [d_levels, d_levels + num_levels) in any way. The ranges [d_levels, d_levels + num_levels) and [d_samples, d_samples + num_samples) may overlap.
When d_temp_storage is nullptr, no work is done and the required allocation size is returned in temp_storage_bytes.

Snippet

The code snippet below illustrates the computation of an six-bin histogram from a sequence of float samples

#include <cub/cub.cuh> // or equivalently <cub/device/device_histogram.cuh>

// Declare, allocate, and initialize device-accessible pointers for input
// samples and output histogram
int      num_samples;    // e.g., 10
float*   d_samples;      // e.g., [2.2, 6.0, 7.1, 2.9, 3.5, 0.3, 2.9, 2.0, 6.1, 999.5]
int*     d_histogram;    // e.g., [ -, -, -, -, -, -]
int      num_levels      // e.g., 7 (seven level boundaries for six bins)
float*   d_levels;       // e.g., [0.0, 2.0, 4.0, 6.0, 8.0, 12.0, 16.0]
...

// Determine temporary device storage requirements
void*    d_temp_storage = nullptr;
size_t   temp_storage_bytes = 0;
cub::DeviceHistogram::HistogramRange(
  d_temp_storage, temp_storage_bytes,
  d_samples, d_histogram, num_levels, d_levels, num_samples);

// Allocate temporary storage
cudaMalloc(&d_temp_storage, temp_storage_bytes);

// Compute histograms
cub::DeviceHistogram::HistogramRange(
  d_temp_storage, temp_storage_bytes,
  d_samples, d_histogram, num_levels, d_levels, num_samples);

// d_histogram   <-- [1, 5, 0, 3, 0, 0];

Template Parameters

SampleIteratorT – [inferred] Random-access input iterator type for reading input samples. (may be a simple pointer type)
CounterT – [inferred] Integer type for histogram bin counters
LevelT – [inferred] Type for specifying boundaries (levels)
OffsetT – [inferred] Signed integer type for sequence offsets, list lengths, pointer differences, etc. (Consider using 32-bit values as offsets/lengths/etc. For example, int will typically yeild better performance than size_t in 64-bit memory mode.)

Parameters

d_temp_storage – [in] Device-accessible allocation of temporary storage. When nullptr, the required allocation size is written to temp_storage_bytes and no work is done.
temp_storage_bytes – [inout] Reference to size in bytes of d_temp_storage allocation
d_samples – [in] The pointer to the input sequence of data samples.
d_histogram – [out] The pointer to the histogram counter output array of length num_levels - 1.
num_levels – [in] The number of boundaries (levels) for delineating histogram samples. Implies that the number of bins is num_levels - 1.
d_levels – [in] The pointer to the array of boundaries (levels). Bin ranges are defined by consecutive boundary pairings: lower sample value boundaries are inclusive and upper sample value boundaries are exclusive.
num_samples – [in] The number of data samples per row in the region of interest
stream – [in]
[optional] CUDA stream to launch kernels within. Default is stream₀.

template<typename SampleIteratorT, typename CounterT, typename LevelT, typename OffsetT> static inline cudaError_t HistogramRange(void *d_temp_storage, size_t &temp_storage_bytes, SampleIteratorT d_samples, CounterT *d_histogram, int num_levels, const LevelT *d_levels, OffsetT num_samples, cudaStream_t stream, bool debug_synchronous)

template<typename SampleIteratorT, typename CounterT, typename LevelT, typename OffsetT> static inline cudaError_t HistogramRange(void *d_temp_storage, size_t &temp_storage_bytes, SampleIteratorT d_samples, CounterT *d_histogram, int num_levels, const LevelT *d_levels, OffsetT num_row_samples, OffsetT num_rows, size_t row_stride_bytes, cudaStream_t stream = 0)

Computes an intensity histogram from a sequence of data samples using the specified bin boundary levels.

A two-dimensional region of interest within d_samples can be specified using the num_row_samples, num_rows, and row_stride_bytes parameters.
The row stride must be a whole multiple of the sample data type size, i.e., (row_stride_bytes % sizeof(SampleT)) == 0.
The number of histogram bins is (num_levels - 1)
The value range for bin_i is [level[i], level[i+1])
For a given row r in [0, num_rows), let row_begin = d_samples + r * row_stride_bytes / sizeof(SampleT) and row_end = row_begin + num_row_samples. The range [d_histogram, d_histogram + num_levels - 1) shall not overlap [row_begin, row_end) nor [d_levels, d_levels + num_levels). The ranges [d_levels, d_levels + num_levels) and [row_begin, row_end) may overlap.
When d_temp_storage is nullptr, no work is done and the required allocation size is returned in temp_storage_bytes.

Snippet

The code snippet below illustrates the computation of a six-bin histogram from a 2x5 region of interest within a flattened 2x7 array of float samples.

#include <cub/cub.cuh> // or equivalently <cub/device/device_histogram.cuh>

// Declare, allocate, and initialize device-accessible pointers for input samples and
// output histogram
int      num_row_samples;    // e.g., 5
int      num_rows;           // e.g., 2;
int      row_stride_bytes;   // e.g., 7 * sizeof(float)
float*   d_samples;          // e.g., [2.2, 6.0, 7.1, 2.9, 3.5,   -, -,
                             //        0.3, 2.9, 2.0, 6.1, 999.5, -, -]
int*     d_histogram;        // e.g., [ -, -, -, -, -, -]
int      num_levels          // e.g., 7 (seven level boundaries for six bins)
float    *d_levels;          // e.g., [0.0, 2.0, 4.0, 6.0, 8.0, 12.0, 16.0]
...

// Determine temporary device storage requirements
void*    d_temp_storage = nullptr;
size_t   temp_storage_bytes = 0;
cub::DeviceHistogram::HistogramRange(
  d_temp_storage, temp_storage_bytes,
  d_samples, d_histogram, num_levels, d_levels,
  num_row_samples, num_rows, row_stride_bytes);

// Allocate temporary storage
cudaMalloc(&d_temp_storage, temp_storage_bytes);

// Compute histograms
cub::DeviceHistogram::HistogramRange(
  d_temp_storage, temp_storage_bytes,
  d_samples, d_histogram, num_levels, d_levels,
  num_row_samples, num_rows, row_stride_bytes);

// d_histogram   <-- [1, 5, 0, 3, 0, 0];

Template Parameters

SampleIteratorT – [inferred] Random-access input iterator type for reading input samples. (may be a simple pointer type)
CounterT – [inferred] Integer type for histogram bin counters
LevelT – [inferred] Type for specifying boundaries (levels)
OffsetT – [inferred] Signed integer type for sequence offsets, list lengths, pointer differences, etc. (Consider using 32-bit values as offsets/lengths/etc. For example, int will typically yeild better performance than size_t in 64-bit memory mode.)

Parameters

d_temp_storage – [in] Device-accessible allocation of temporary storage. When nullptr, the required allocation size is written to temp_storage_bytes and no work is done.
temp_storage_bytes – [inout] Reference to size in bytes of d_temp_storage allocation
d_samples – [in] The pointer to the input sequence of data samples.
d_histogram – [out] The pointer to the histogram counter output array of length num_levels - 1.
num_levels – [in] The number of boundaries (levels) for delineating histogram samples. Implies that the number of bins is num_levels - 1.
d_levels – [in] The pointer to the array of boundaries (levels). Bin ranges are defined by consecutive boundary pairings: lower sample value boundaries are inclusive and upper sample value boundaries are exclusive.
num_row_samples – [in] The number of data samples per row in the region of interest
num_rows – [in] The number of rows in the region of interest
row_stride_bytes – [in] The number of bytes between starts of consecutive rows in the region of interest
stream – [in]
[optional] CUDA stream to launch kernels within. Default is stream₀.

template<typename SampleIteratorT, typename CounterT, typename LevelT, typename OffsetT> static inline cudaError_t HistogramRange(void *d_temp_storage, size_t &temp_storage_bytes, SampleIteratorT d_samples, CounterT *d_histogram, int num_levels, const LevelT *d_levels, OffsetT num_row_samples, OffsetT num_rows, size_t row_stride_bytes, cudaStream_t stream, bool debug_synchronous)

template<int NUM_CHANNELS, int NUM_ACTIVE_CHANNELS, typename SampleIteratorT, typename CounterT, typename LevelT, typename OffsetT> static inline cudaError_t MultiHistogramRange(void *d_temp_storage, size_t &temp_storage_bytes, SampleIteratorT d_samples, CounterT *d_histogram[NUM_ACTIVE_CHANNELS], const int num_levels[NUM_ACTIVE_CHANNELS], const LevelT *const d_levels[NUM_ACTIVE_CHANNELS], OffsetT num_pixels, cudaStream_t stream = 0)

Computes per-channel intensity histograms from a sequence of multi-channel “pixel” data samples using the specified bin boundary levels.

The input is a sequence of pixel structures, where each pixel comprises a record of NUM_CHANNELS consecutive data samples (e.g., an RGBA pixel).
NUM_CHANNELS can be up to 4.
Of the NUM_CHANNELS specified, the function will only compute histograms for the first NUM_ACTIVE_CHANNELS (e.g., RGB histograms from RGBA pixel samples).
The number of histogram bins for channel_i is num_levels[i] - 1.
For channel_i, the range of values for all histogram bins have the same width: (upper_level[i] - lower_level[i]) / (num_levels[i] - 1)
For given channels c1 and c2 in [0, NUM_ACTIVE_CHANNELS), the range [d_histogram[c1], d_histogram[c1] + num_levels[c1] - 1) shall not overlap [d_samples, d_samples + NUM_CHANNELS * num_pixels) nor [d_levels[c2], d_levels[c2] + num_levels[c2]) in any way. The ranges [d_levels[c2], d_levels[c2] + num_levels[c2]) and [d_samples, d_samples + NUM_CHANNELS * num_pixels) may overlap.
When d_temp_storage is nullptr, no work is done and the required allocation size is returned in temp_storage_bytes.

Snippet

The code snippet below illustrates the computation of three 4-bin RGB histograms from a quad-channel sequence of RGBA pixels (8 bits per channel per pixel)

#include <cub/cub.cuh> // or equivalently <cub/device/device_histogram.cuh>

// Declare, allocate, and initialize device-accessible pointers for
// input samples and output histograms
int            num_pixels;       // e.g., 5
unsigned char  *d_samples;       // e.g., [(2, 6, 7, 5),(3, 0, 2, 1),(7, 0, 6, 2),
                                 //        (0, 6, 7, 5),(3, 0, 2, 6)]
unsigned int   *d_histogram[3];  // e.g., [[ -, -, -, -],[ -, -, -, -],[ -, -, -, -]];
int            num_levels[3];    // e.g., {5, 5, 5};
unsigned int   *d_levels[3];     // e.g., [ [0, 2, 4, 6, 8],
                                 //         [0, 2, 4, 6, 8],
                                 //         [0, 2, 4, 6, 8] ];
...

// Determine temporary device storage requirements
void*    d_temp_storage = nullptr;
size_t   temp_storage_bytes = 0;
cub::DeviceHistogram::MultiHistogramRange<4, 3>(
  d_temp_storage, temp_storage_bytes,
  d_samples, d_histogram, num_levels, d_levels, num_pixels);

// Allocate temporary storage
cudaMalloc(&d_temp_storage, temp_storage_bytes);

// Compute histograms
cub::DeviceHistogram::MultiHistogramRange<4, 3>(
  d_temp_storage, temp_storage_bytes,
  d_samples, d_histogram, num_levels, d_levels, num_pixels);

// d_histogram   <-- [ [1, 3, 0, 1],
//                     [3, 0, 0, 2],
//                     [0, 2, 0, 3] ]

Template Parameters

NUM_CHANNELS – Number of channels interleaved in the input data (may be greater than the number of channels being actively histogrammed)
NUM_ACTIVE_CHANNELS – [inferred] Number of channels actively being histogrammed
SampleIteratorT – [inferred] Random-access input iterator type for reading input samples. (may be a simple pointer type)
CounterT – [inferred] Integer type for histogram bin counters
LevelT – [inferred] Type for specifying boundaries (levels)
OffsetT – [inferred] Signed integer type for sequence offsets, list lengths, pointer differences, etc. (Consider using 32-bit values as offsets/lengths/etc. For example, int will typically yeild better performance than size_t in 64-bit memory mode.)

Parameters

d_temp_storage – [in] Device-accessible allocation of temporary storage. When nullptr, the required allocation size is written to temp_storage_bytes and no work is done.
temp_storage_bytes – [inout] Reference to size in bytes of d_temp_storage allocation
d_samples – [in] The pointer to the multi-channel input sequence of data samples. The samples from different channels are assumed to be interleaved (e.g., an array of 32-bit pixels where each pixel consists of four RGBA 8-bit samples).
d_histogram – [out]
The pointers to the histogram counter output arrays, one for each active channel. For channel_i, the allocation length of d_histogram[i] should be num_levels[i] - 1.
num_levels – [in]
The number of boundaries (levels) for delineating histogram samples in each active channel. Implies that the number of bins for channel_i is num_levels[i] - 1.
d_levels – [in] The pointers to the arrays of boundaries (levels), one for each active channel. Bin ranges are defined by consecutive boundary pairings: lower sample value boundaries are inclusive and upper sample value boundaries are exclusive.
num_pixels – [in] The number of multi-channel pixels (i.e., the length of d_samples / NUM_CHANNELS)
stream – [in]
[optional] CUDA stream to launch kernels within. Default is stream₀.

template<int NUM_CHANNELS, int NUM_ACTIVE_CHANNELS, typename SampleIteratorT, typename CounterT, typename LevelT, typename OffsetT> static inline cudaError_t MultiHistogramRange(void *d_temp_storage, size_t &temp_storage_bytes, SampleIteratorT d_samples, CounterT *d_histogram[NUM_ACTIVE_CHANNELS], const int num_levels[NUM_ACTIVE_CHANNELS], const LevelT *const d_levels[NUM_ACTIVE_CHANNELS], OffsetT num_pixels, cudaStream_t stream, bool debug_synchronous)

template<int NUM_CHANNELS, int NUM_ACTIVE_CHANNELS, typename SampleIteratorT, typename CounterT, typename LevelT, typename OffsetT> static inline cudaError_t MultiHistogramRange(void *d_temp_storage, size_t &temp_storage_bytes, SampleIteratorT d_samples, CounterT *d_histogram[NUM_ACTIVE_CHANNELS], const int num_levels[NUM_ACTIVE_CHANNELS], const LevelT *const d_levels[NUM_ACTIVE_CHANNELS], OffsetT num_row_pixels, OffsetT num_rows, size_t row_stride_bytes, cudaStream_t stream = 0)

Computes per-channel intensity histograms from a sequence of multi-channel “pixel” data samples using the specified bin boundary levels.

The input is a sequence of pixel structures, where each pixel comprises a record of NUM_CHANNELS consecutive data samples (e.g., an RGBA pixel).
NUM_CHANNELS can be up to 4.
Of the NUM_CHANNELS specified, the function will only compute histograms for the first NUM_ACTIVE_CHANNELS (e.g., RGB histograms from RGBA pixel samples).
A two-dimensional region of interest within d_samples can be specified using the num_row_samples, num_rows, and row_stride_bytes parameters.
The row stride must be a whole multiple of the sample data type size, i.e., (row_stride_bytes % sizeof(SampleT)) == 0.
The number of histogram bins for channel_i is num_levels[i] - 1.
For channel_i, the range of values for all histogram bins have the same width: (upper_level[i] - lower_level[i]) / (num_levels[i] - 1)
For a given row r in [0, num_rows), and sample s in [0, num_row_pixels), let row_begin = d_samples + r * row_stride_bytes / sizeof(SampleT), sample_begin = row_begin + s * NUM_CHANNELS, and sample_end = sample_begin + NUM_ACTIVE_CHANNELS. For given channels c1 and c2 in [0, NUM_ACTIVE_CHANNELS), the range [d_histogram[c1], d_histogram[c1] + num_levels[c1] - 1) shall not overlap [sample_begin, sample_end) nor [d_levels[c2], d_levels[c2] + num_levels[c2]) in any way. The ranges [d_levels[c2], d_levels[c2] + num_levels[c2]) and [sample_begin, sample_end) may overlap.
When d_temp_storage is nullptr, no work is done and the required allocation size is returned in temp_storage_bytes.

Snippet

The code snippet below illustrates the computation of three 4-bin RGB histograms from a 2x3 region of interest of within a flattened 2x4 array of quad-channel RGBA pixels (8 bits per channel per pixel).

#include <cub/cub.cuh> // or equivalently <cub/device/device_histogram.cuh>

// Declare, allocate, and initialize device-accessible pointers for input
// samples and output histograms
int              num_row_pixels;     // e.g., 3
int              num_rows;           // e.g., 2
size_t           row_stride_bytes;   // e.g., 4 * sizeof(unsigned char) * NUM_CHANNELS
unsigned char*   d_samples;          // e.g., [(2, 6, 7, 5),(3, 0, 2, 1),(1, 1, 1, 1),(-, -, -, -),
                                     //        (7, 0, 6, 2),(0, 6, 7, 5),(3, 0, 2, 6),(-, -, -, -)]
int*             d_histogram[3];     // e.g., [[ -, -, -, -],[ -, -, -, -],[ -, -, -, -]];
int              num_levels[3];      // e.g., {5, 5, 5};
unsigned int*    d_levels[3];        // e.g., [ [0, 2, 4, 6, 8],
                                     //         [0, 2, 4, 6, 8],
                                     //         [0, 2, 4, 6, 8] ];
...

// Determine temporary device storage requirements
void*    d_temp_storage = nullptr;
size_t   temp_storage_bytes = 0;
cub::DeviceHistogram::MultiHistogramRange<4, 3>(
  d_temp_storage, temp_storage_bytes,
  d_samples, d_histogram, num_levels, d_levels,
  num_row_pixels, num_rows, row_stride_bytes);

// Allocate temporary storage
cudaMalloc(&d_temp_storage, temp_storage_bytes);

// Compute histograms
cub::DeviceHistogram::MultiHistogramRange<4, 3>(
  d_temp_storage, temp_storage_bytes,
  d_samples, d_histogram, num_levels,
  d_levels, num_row_pixels, num_rows, row_stride_bytes);

// d_histogram   <-- [ [2, 3, 0, 1],
//                     [3, 0, 0, 2],
//                     [1, 2, 0, 3] ]

Template Parameters

NUM_CHANNELS – Number of channels interleaved in the input data (may be greater than the number of channels being actively histogrammed)
NUM_ACTIVE_CHANNELS – [inferred] Number of channels actively being histogrammed
SampleIteratorT – [inferred] Random-access input iterator type for reading input samples. (may be a simple pointer type)
CounterT – [inferred] Integer type for histogram bin counters
LevelT – [inferred] Type for specifying boundaries (levels)
OffsetT – [inferred] Signed integer type for sequence offsets, list lengths, pointer differences, etc. (Consider using 32-bit values as offsets/lengths/etc. For example, int will typically yeild better performance than size_t in 64-bit memory mode.)

Parameters

d_temp_storage – [in] Device-accessible allocation of temporary storage. When nullptr, the required allocation size is written to temp_storage_bytes and no work is done.
temp_storage_bytes – [inout] Reference to size in bytes of d_temp_storage allocation
d_samples – [in] The pointer to the multi-channel input sequence of data samples. The samples from different channels are assumed to be interleaved (e.g., an array of 32-bit pixels where each pixel consists of four RGBA 8-bit samples).
d_histogram – [out]
The pointers to the histogram counter output arrays, one for each active channel. For channel_i, the allocation length of d_histogram[i] should be num_levels[i] - 1.
num_levels – [in]
The number of boundaries (levels) for delineating histogram samples in each active channel. Implies that the number of bins for channel_i is num_levels[i] - 1.
d_levels – [in] The pointers to the arrays of boundaries (levels), one for each active channel. Bin ranges are defined by consecutive boundary pairings: lower sample value boundaries are inclusive and upper sample value boundaries are exclusive.
num_row_pixels – [in] The number of multi-channel pixels per row in the region of interest
num_rows – [in] The number of rows in the region of interest
row_stride_bytes – [in] The number of bytes between starts of consecutive rows in the region of interest
stream – [in]
[optional] CUDA stream to launch kernels within. Default is stream₀.

template<int NUM_CHANNELS, int NUM_ACTIVE_CHANNELS, typename SampleIteratorT, typename CounterT, typename LevelT, typename OffsetT> static inline cudaError_t MultiHistogramRange(void *d_temp_storage, size_t &temp_storage_bytes, SampleIteratorT d_samples, CounterT *d_histogram[NUM_ACTIVE_CHANNELS], const int num_levels[NUM_ACTIVE_CHANNELS], const LevelT *const d_levels[NUM_ACTIVE_CHANNELS], OffsetT num_row_pixels, OffsetT num_rows, size_t row_stride_bytes, cudaStream_t stream, bool debug_synchronous)