Open VKL API#

To access the Open VKL API you first need to include the Open VKL header. For C99 or C++:

#include <openvkl/openvkl.h>


For the Intel® Implicit SPMD Program Compiler (Intel® ISPC):

#include <openvkl/openvkl.isph>


This documentation will discuss the C99/C++ API. The ISPC version has the same functionality and flavor. Looking at the headers, the vklTutorialISPC example, and this documentation should be enough to figure it out.

Device initialization and shutdown#

To use the API, one of the implemented backends must be loaded. Currently the only one that exists is the CPU device. To load the module that implements the CPU device:

vklLoadModule("cpu_device");


The device then needs to be instantiated:

VKLDevice device = vklNewDevice("cpu");


By default, the CPU device selects the maximum supported SIMD width (and associated ISA) for the system. Optionally, a specific width may be requested using the cpu_4, cpu_8, or cpu_16 device names. Note that the system must support the given width (SSE4.1 for 4-wide, AVX for 8-wide, and AVX512 for 16-wide).

Once a device is created, you can call

void vklDeviceSetInt(VKLDevice, const char *name, int val);
void vklDeviceSetString(VKLDevice, const char *name, const char *val);


to set parameters on the device. The following parameters are understood by all devices:

Parameters shared by all devices.#

Type

Name

Description

int

logLevel

logging level; valid values are VKL_LOG_DEBUG, VKL_LOG_INFO, VKL_LOG_WARNING, VKL_LOG_ERROR and VKL_LOG_NONE

string

logOutput

convenience for setting where log messages go; valid values are cout, cerr and none

string

errorOutput

convenience for setting where error messages go; valid values are cout, cerr and none

int

number of threads which Open VKL can use

int

flushDenormals

sets the Flush to Zero and Denormals are Zero mode of the MXCSR control and status register (default: 1); see Performance Recommendations section for details

Once parameters are set, the device must be committed with

vklCommitDevice(device);


The newly committed device is then ready to use. Users may change parameters on a device after initialization. In this case the device would need to be re-committed.

All Open VKL objects are associated with a device. A device handle must be explicitly provided when creating volume and data objects, via vklNewVolume() and vklNewData() respectively. Other object types are automatically associated with a device via transitive dependency on a volume.

Open VKL provides vector-wide versions for several APIs. To determine the native vector width for a given device, call:

int width = vklGetNativeSIMDWidth(VKLDevice device);


When the application is finished with an Open VKL device or shutting down, release the device via:

vklReleaseDevice(VKLDevice device);


Environment variables#

The generic device parameters can be overridden via environment variables for easy changes to Open VKL’s behavior without needing to change the application (variables are prefixed by convention with “OPENVKL_”):

Environment variables understood by all devices.#

Variable

Description

OPENVKL_LOG_LEVEL

logging level; valid values are debug, info, warning, error and none

OPENVKL_LOG_OUTPUT

convenience for setting where log messages go; valid values are cout, cerr and none

OPENVKL_ERROR_OUTPUT

convenience for setting where error messages go; valid values are cout, cerr and none

number of threads which Open VKL can use

OPENVKL_FLUSH_DENORMALS

sets the Flush to Zero and Denormals are Zero mode of the MXCSR control and status register (default: 1); see Performance Recommendations section for details

Note that these environment variables take precedence over values set through the vklDeviceSet*() functions.

Additionally, the CPU device’s default SIMD width can be overriden at run time with the OPENVKL_CPU_DEVICE_DEFAULT_WIDTH environment variable. Legal values are 4, 8, or 16. This setting is only applicable when the generic cpu device is instantiated; if a specific width is requested via the cpu_[4,8,16] device names then the environment variable is ignored.

Error handling and log messages#

The following errors are currently used by Open VKL:

Possible error codes, i.e., valid named constants of type VKLError.#

Name

Description

VKL_NO_ERROR

no error occurred

VKL_UNKNOWN_ERROR

an unknown error occurred

VKL_INVALID_ARGUMENT

an invalid argument was specified

VKL_INVALID_OPERATION

the operation is not allowed for the specified object

VKL_OUT_OF_MEMORY

there is not enough memory to execute the command

VKL_UNSUPPORTED_CPU

the CPU is not supported (minimum ISA is SSE4.1)

These error codes are either directly returned by some API functions, or are recorded to be later queried by the application via

VKLError vklDeviceGetLastErrorCode(VKLDevice);


A more descriptive error message can be queried by calling

const char* vklDeviceGetLastErrorMsg(VKLDevice);


Alternatively, the application can also register a callback function of type

typedef void (*VKLErrorCallback)(void *, VKLError, const char* message);


via

void vklDeviceSetErrorCallback(VKLDevice, VKLErrorFunc, void *);


to get notified when errors occur. Applications may be interested in messages which Open VKL emits, whether for debugging or logging events. Applications can register a callback function of type

typedef void (*VKLLogCallback)(void *, const char* message);


via

void vklDeviceSetLogCallback(VKLDevice, VKLLogCallback, void *);


which Open VKL will use to emit log messages. Applications can clear either callback by passing nullptr instead of an actual function pointer. By default, Open VKL uses cout and cerr to emit log and error messages, respectively. The last parameter to vklDeviceSetErrorCallback and vklDeviceSetLogCallback is a user data pointer. Open VKL passes this pointer to the callback functions as the first parameter. Note that in addition to setting the above callbacks, this behavior can be changed via the device parameters and environment variables described previously.

Basic data types#

Open VKL defines 3-component vectors of integer and vector types:

typedef struct
{
int x, y, z;
} vkl_vec3i;

typedef struct
{
float x, y, z;
} vkl_vec3f;


Vector versions of these are also defined in structure-of-array format for 4, 8, and 16 wide types.

typedef struct
{
float x[WIDTH];
float y[WIDTH];
float z[WIDTH];
} vkl_vvec3f##WIDTH;

typedef struct
{
float lower[WIDTH], upper[WIDTH];
} vkl_vrange1f##WIDTH;


1-D range and 3-D ranges are defined as ranges and boxes, with no vector versions:

typedef struct
{
float lower, upper;
} vkl_range1f;

typedef struct
{
vkl_vec3f lower, upper;
} vkl_box3f;


Object model#

Objects in Open VKL are exposed to the APIs as handles with internal reference counting for lifetime determination. Objects are created with particular type’s vklNew... API entry point. For example, vklNewData and vklNewVolume.

In general, modifiable parameters to objects are modified using vklSet... functions based on the type of the parameter being set. The parameter name is passed as a string. Below are all variants of vklSet....

void vklSetBool(VKLObject object, const char *name, int b);
void vklSetFloat(VKLObject object, const char *name, float x);
void vklSetVec3f(VKLObject object, const char *name, float x, float y, float z);
void vklSetInt(VKLObject object, const char *name, int x);
void vklSetVec3i(VKLObject object, const char *name, int x, int y, int z);
void vklSetData(VKLObject object, const char *name, VKLData data);
void vklSetString(VKLObject object, const char *name, const char *s);
void vklSetVoidPtr(VKLObject object, const char *name, void *v);


After parameters have been set, vklCommit must be called on the object to make them take effect.

Open VKL uses reference counting to manage the lifetime of all objects. Therefore one cannot explicitly “delete” any object. Instead, one can indicate the application does not need or will not access the given object anymore by calling

void vklRelease(VKLObject);


This decreases the object’s reference count. If the count reaches 0 the object will automatically be deleted.

Managed data#

Large data is passed to Open VKL via a VKLData handle created with vklNewData:

VKLData vklNewData(VKLDevice device,
size_t numItems,
VKLDataType dataType,
const void *source,
VKLDataCreationFlags dataCreationFlags,
size_t byteStride);


Data objects can be created as Open VKL owned (dataCreationFlags = VKL_DATA_DEFAULT), in which the library will make a copy of the data for its use, or shared (dataCreationFlags = VKL_DATA_SHARED_BUFFER), which will try to use the passed pointer for usage. The library is allowed to copy data when a volume is committed.

The distance between consecutive elements in source is given in bytes with byteStride. If the provided byteStride is zero, then it will be determined automatically as sizeof(type). Open VKL owned data will be compacted into a naturally-strided array on copy, regardless of the original byteStride.

As with other object types, when data objects are no longer needed they should be released via vklRelease.

The enum type VKLDataType describes the different element types that can be represented in Open VKL. The types accepted vary per volume; see the volume section for specifics. Valid constants are listed in the table below.

Valid named constants for VKLDataType.#

Type/Name

Description

VKL_DEVICE

API device object reference

VKL_DATA

data reference

VKL_OBJECT

generic object reference

VKL_VOLUME

volume object reference

VKL_STRING

C-style zero-terminated character string

VKL_CHAR, VKL_VEC[234]C

8 bit signed character scalar and [234]-element vector

VKL_UCHAR, VKL_VEC[234]UC

8 bit unsigned character scalar and [234]-element vector

VKL_SHORT, VKL_VEC[234]S

16 bit unsigned integer scalar and [234]-element vector

VKL_USHORT, VKL_VEC[234]US

16 bit unsigned integer scalar and [234]-element vector

VKL_INT, VKL_VEC[234]I

32 bit signed integer scalar and [234]-element vector

VKL_UINT, VKL_VEC[234]UI

32 bit unsigned integer scalar and [234]-element vector

VKL_LONG, VKL_VEC[234]L

64 bit signed integer scalar and [234]-element vector

VKL_ULONG, VKL_VEC[234]UL

64 bit unsigned integer scalar and [234]-element vector

VKL_HALF, VKL_VEC[234]H

16 bit half precision floating-point scalar and [234]-element vector (IEEE 754 binary16)

VKL_FLOAT, VKL_VEC[234]F

32 bit single precision floating-point scalar and [234]-element vector

VKL_DOUBLE, VKL_VEC[234]D

64 bit double precision floating-point scalar and [234]-element vector

VKL_BOX[1234]I

32 bit integer box (lower + upper bounds)

VKL_BOX[1234]F

32 bit single precision floating-point box (lower + upper bounds)

VKL_LINEAR[23]F

32 bit single precision floating-point linear transform ([23] vectors)

VKL_AFFINE[23]F

32 bit single precision floating-point affine transform (linear transform plus translation)

VKL_VOID_PTR

Observers#

Volumes and samplers in Open VKL may provide observers to communicate data back to the application. Observers may be created with

VKLObserver vklNewSamplerObserver(VKLSampler sampler,
const char *type);

VKLObserver vklNewVolumeObserver(VKLVolume volume,
const char *type);


The object passed to vklNew*Observer must already be committed. Valid observer type strings are defined by volume implementations (see section ‘Volume types’ below).

vklNew*Observer returns NULL on failure.

To access the underlying data, an observer must first be mapped using

const void * vklMapObserver(VKLObserver observer);


If this fails, the function returns NULL. vklMapObserver may fail on observers that are already mapped. On success, the application may query the underlying type, element size in bytes, and the number of elements in the buffer using

VKLDataType vklGetObserverElementType(VKLObserver observer);
size_t vklGetObserverElementSize(VKLObserver observer);
size_t vklGetObserverNumElements(VKLObserver observer);


On failure, these functions return VKL_UNKNOWN and 0, respectively. Possible data types are defined by the volume that provides the observer , as are the semantics of the observation. See section ‘Volume types’ for details.

The pointer returned by vklMapObserver may be cast to the type corresponding to the value returned by vklGetObserverElementType to access the observation. For example, if vklGetObserverElementType returns VKL_FLOAT, then the pointer returned by vklMapObserver may be cast to const float * to access up to vklGetObserverNumElements consecutive values of type float.

Once the application has finished processing the observation, it should unmap the observer using

void vklUnmapObserver(VKLObserver observer);


so that the observer may be mapped again.

When an observer is no longer needed, it should be released using vklRelease.

The observer API is not thread safe, and these functions should not be called concurrently on the same object.

Volume types#

Open VKL currently supports structured volumes on regular and spherical grids; unstructured volumes with tetrahedral, wedge, pyramid, and hexaderal primitive types; adaptive mesh refinement (AMR) volumes; sparse VDB volumes; and particle volumes. Volumes are created with vklNewVolume with a device and appropriate type string:

VKLVolume vklNewVolume(VKLDevice device, const char *type);


In addition to the usual vklSet...() and vklCommit() APIs, the volume bounding box can be queried:

vkl_box3f vklGetBoundingBox(VKLVolume volume);


The number of attributes in a volume can also be queried:

unsigned int vklGetNumAttributes(VKLVolume volume);


Finally, the value range of the volume for a given attribute can be queried:

vkl_range1f vklGetValueRange(VKLVolume volume, unsigned int attributeIndex);


Structured Volumes#

Structured volumes only need to store the values of the samples, because their addresses in memory can be easily computed from a 3D position. The dimensions for all structured volume types are in units of vertices, not cells. For example, a volume with dimensions $$(x, y, z)$$ will have $$(x-1, y-1, z-1)$$ cells in each dimension. Voxel data provided is assumed vertex-centered, so $$x*y*z$$ values must be provided.

Structured Regular Volumes#

A common type of structured volumes are regular grids, which are created by passing a type string of "structuredRegular" to vklNewVolume. The parameters understood by structured regular volumes are summarized in the table below.

Configuration parameters for structured regular ("structuredRegular") volumes.#

Type

Name

Default

Description

vec3i

dimensions

number of voxels in each dimension $$(x, y, z)$$

VKLData VKLData[]

data

VKLData object(s) of voxel data, supported types are:

VKL_UCHAR

VKL_SHORT

VKL_USHORT

VKL_HALF

VKL_FLOAT

VKL_DOUBLE

Multiple attributes are supported through passing an array of VKLData objects.

vec3f

gridOrigin

$$(0, 0, 0)$$

origin of the grid in object space

vec3f

gridSpacing

$$(1, 1, 1)$$

size of the grid cells in object space

uint32

temporalFormat

VKL_TEMPORAL_FORMAT_CONSTANT

The temporal format for this volume. Use VKLTemporalFormat for named constants. Structured regular volumes support VKL_TEMPORAL_FORMAT_CONSTANT, VKL_TEMPORAL_FORMAT_STRUCTURED, and VKL_TEMPORAL_FORMAT_UNSTRUCTURED.

int

temporallyStructuredNumTimesteps

For temporally structured variation, number of timesteps per voxel. Only valid if temporalFormat is VKL_TEMPORAL_FORMAT_STRUCTURED.

uint32[] uint64[]

temporallyUnstructuredIndices

For temporally unstructured variation, indices to data time series beginning per voxel. Only valid if temporalFormat is VKL_TEMPORAL_FORMAT_UNSTRUCTURED.

float[]

temporallyUnstructuredTimes

For temporally unstructured variation, time values corresponding to values in data. Only valid if temporalFormat is VKL_TEMPORAL_FORMAT_UNSTRUCTURED.

float[]

background

VKL_BACKGROUND_UNDEFINED

For each attribute, the value that is returned when sampling an undefined region outside the volume domain.

Structured regular volumes support temporally structured and temporally unstructured temporal variation. See section ‘Temporal Variation’ for more detail.

The following additional parameters can be set both on "structuredRegular" volumes and their sampler objects. Sampler object parameters default to volume parameters.

Configuration parameters for structured regular ("structuredRegular") volumes and their sampler objects.#

Type

Name

Default

Description

int

filter

VKL_FILTER_TRILINEAR

The filter used for reconstructing the field. Use VKLFilter for named constants.

int

filter

The filter used for reconstructing the field during gradient computations. Use VKLFilter for named constants.

Reconstruction filters#

Structured regular volumes support the filter types VKL_FILTER_NEAREST, VKL_FILTER_TRILINEAR, and VKL_FILTER_TRICUBIC for both filter and gradientFilter.

Note that when gradientFilter is set to VKL_FILTER_NEAREST, gradients are always $$(0, 0, 0)$$.

Structured Spherical Volumes#

Structured spherical volumes are also supported, which are created by passing a type string of "structuredSpherical" to vklNewVolume. The grid dimensions and parameters are defined in terms of radial distance ($$r$$), inclination angle ($$\theta$$), and azimuthal angle ($$\phi$$), conforming with the ISO convention for spherical coordinate systems. The coordinate system and parameters understood by structured spherical volumes are summarized below.

Configuration parameters for structured spherical ("structuredSpherical") volumes.#

Type

Name

Default

Description

vec3i

dimensions

number of voxels in each dimension $$(r, \theta, \phi)$$

VKLData VKLData[]

data

VKLData object(s) of voxel data, supported types are:

VKL_UCHAR

VKL_SHORT

VKL_USHORT

VKL_HALF

VKL_FLOAT

VKL_DOUBLE

Multiple attributes are supported through passing an array of VKLData objects.

vec3f

gridOrigin

$$(0, 0, 0)$$

origin of the grid in units of $$(r, \theta, \phi)$$; angles in degrees

vec3f

gridSpacing

$$(1, 1, 1)$$

size of the grid cells in units of $$(r, \theta, \phi)$$; angles in degrees

float[]

background

VKL_BACKGROUND_UNDEFINED

For each attribute, the value that is returned when sampling an undefined region outside the volume domain.

These grid parameters support flexible specification of spheres, hemispheres, spherical shells, spherical wedges, and so forth. The grid extents (computed as $$[gridOrigin, gridOrigin + (dimensions - 1) * gridSpacing]$$) however must be constrained such that:

• $$r \geq 0$$

• $$0 \leq \theta \leq 180$$

• $$0 \leq \phi \leq 360$$

The following additional parameters can be set both on "structuredSpherical" volumes and their sampler objects. Sampler object parameters default to volume parameters.

Configuration parameters for structured spherical ("structuredSpherical") volumes and their sampler objects.#

Type

Name

Default

Description

int

filter

VKL_FILTER_TRILINEAR

The filter used for reconstructing the field. Use VKLFilter for named constants.

int

filter

The filter used for reconstructing the field during gradient computations. Use VKLFilter for named constants.

Open VKL currently supports block-structured (Berger-Colella) AMR volumes. Volumes are specified as a list of blocks, which exist at levels of refinement in potentially overlapping regions. Blocks exist in a tree structure, with coarser refinement level blocks containing finer blocks. The cell width is equal for all blocks at the same refinement level, though blocks at a coarser level have a larger cell width than finer levels.

There can be any number of refinement levels and any number of blocks at any level of refinement.

Blocks are defined by three parameters: their bounds, the refinement level in which they reside, and the scalar data contained within each block.

Note that cell widths are defined per refinement level, not per block.

AMR volumes are created by passing the type string "amr" to vklNewVolume, and have the following parameters:

Configuration parameters for AMR ("amr") volumes.#

Type

Name

Default

Description

float[]

cellWidth

[data] array of each level’s cell width

box3i[]

block.bounds

[data] array of each block’s bounds (in voxels)

int[]

block.level

[data] array of each block’s refinement level

VKLData[]

block.data

[data] array of each block’s VKLData object containing the actual scalar voxel data. Currently only VKL_FLOAT data is supported.

vec3f

gridOrigin

$$(0, 0, 0)$$

origin of the grid in object space

vec3f

gridSpacing

$$(1, 1, 1)$$

size of the grid cells in object space

float

background

VKL_BACKGROUND_UNDEFINED

The value that is returned when sampling an undefined region outside the volume domain.

Note that the gridOrigin and gridSpacing parameters act just like the structured volume equivalent, but they only modify the root (coarsest level) of refinement.

The following additional parameters can be set both on "amr" volumes and their sampler objects. Sampler object parameters default to volume parameters.

Configuration parameters for AMR ("AMR") volumes and their sampler objects.#

Type

Name

Default

Description

VKLAMRMethod

method

VKL_AMR_CURRENT

VKLAMRMethod sampling method. Supported methods are:

VKL_AMR_CURRENT

VKL_AMR_FINEST

VKL_AMR_OCTANT

Open VKL’s AMR implementation was designed to cover Berger-Colella [1] and Chombo [2] AMR data. The method parameter above determines the interpolation method used when sampling the volume.

• VKL_AMR_CURRENT finds the finest refinement level at that cell and interpolates through this “current” level

• VKL_AMR_FINEST will interpolate at the closest existing cell in the volume-wide finest refinement level regardless of the sample cell’s level

• VKL_AMR_OCTANT interpolates through all available refinement levels at that cell. This method avoids discontinuities at refinement level boundaries at the cost of performance

Gradients are computed using finite differences, using the method defined on the sampler.

Details and more information can be found in the publication for the implementation [3].

1. Berger, and P. Colella. “Local adaptive mesh refinement for shock hydrodynamics.” Journal of Computational Physics 82.1 (1989): 64-84. DOI: 10.1016/0021-9991(89)90035-1

1. Adams, P. Colella, D. T. Graves, J.N. Johnson, N.D. Keen, T. J. Ligocki. D. F. Martin. P.W. McCorquodale, D. Modiano. P.O. Schwartz, T.D. Sternberg and B. Van Straalen, Chombo Software Package for AMR Applications - Design Document, Lawrence Berkeley National Laboratory Technical Report LBNL-6616E.

1. Wald, C. Brownlee, W. Usher, and A. Knoll. CPU volume rendering of adaptive mesh refinement data. SIGGRAPH Asia 2017 Symposium on Visualization on - SA ’17, 18(8), 1–8. DOI: 10.1145/3139295.3139305

Unstructured Volumes#

Unstructured volumes can have their topology and geometry freely defined. Geometry can be composed of tetrahedral, hexahedral, wedge or pyramid cell types. The data format used is compatible with VTK and consists of multiple arrays: vertex positions and values, vertex indices, cell start indices, cell types, and cell values.

Sampled cell values can be specified either per-vertex (vertex.data) or per-cell (cell.data). If both arrays are set, cell.data takes precedence.

Similar to a mesh, each cell is formed by a group of indices into the vertices. For each vertex, the corresponding (by array index) data value will be used for sampling when rendering, if specified. The index order for a tetrahedron is the same as VTK_TETRA: bottom triangle counterclockwise, then the top vertex.

For hexahedral cells, each hexahedron is formed by a group of eight indices into the vertices and data values. Vertex ordering is the same as VTK_HEXAHEDRON: four bottom vertices counterclockwise, then top four counterclockwise.

For wedge cells, each wedge is formed by a group of six indices into the vertices and data values. Vertex ordering is the same as VTK_WEDGE: three bottom vertices counterclockwise, then top three counterclockwise.

For pyramid cells, each cell is formed by a group of five indices into the vertices and data values. Vertex ordering is the same as VTK_PYRAMID: four bottom vertices counterclockwise, then the top vertex.

To maintain VTK data compatibility, the index array may be specified with cell sizes interleaved with vertex indices in the following format: $$n, id_1, ..., id_n, m, id_1, ..., id_m$$. This alternative index array layout can be enabled through the indexPrefixed flag (in which case, the cell.type parameter should be omitted).

Gradients are computed using finite differences.

Unstructured volumes are created by passing the type string "unstructured" to vklNewVolume, and have the following parameters:

Configuration parameters for unstructured ("unstructured") volumes.#

Type

Name

Default

Description

vec3f[]

vertex.position

[data] array of vertex positions

float[]

vertex.data

[data] array of vertex data values to be sampled

uint32[] / uint64[]

index

[data] array of indices (into the vertex array(s)) that form cells

bool

indexPrefixed

false

indicates that the index array is provided in a VTK-compatible format, where the indices of each cell are prefixed with the number of vertices

uint32[] / uint64[]

cell.index

[data] array of locations (into the index array), specifying the first index of each cell

float[]

cell.data

[data] array of cell data values to be sampled

uint8[]

cell.type

[data] array of cell types (VTK compatible). Supported types are:

VKL_TETRAHEDRON

VKL_HEXAHEDRON

VKL_WEDGE

VKL_PYRAMID

bool

hexIterative

false

hexahedron interpolation method, defaults to fast non-iterative version which could have rendering inaccuracies may appear if hex is not parallelepiped

bool

precomputedNormals

false

whether to accelerate by precomputing, at a cost of 12 bytes/face

float

background

VKL_BACKGROUND_UNDEFINED

The value that is returned when sampling an undefined region outside the volume domain.

VDB Volumes#

VDB volumes implement a data structure that is very similar to the data structure outlined in Museth [1].

The data structure is a hierarchical regular grid at its core: Nodes are regular grids, and each grid cell may either store a constant value (this is called a tile), or child pointers.

Nodes in VDB trees are wide: Nodes on the first level have a resolution of 32^3 voxels by default, on the next level 16^3, and on the leaf level 8^3 voxels. All nodes on a given level have the same resolution. This makes it easy to find the node containing a coordinate using shift operations (cp. [1]).

VDB leaf nodes are implicit in Open VKL: they are stored as pointers to user-provided data.

VDB volumes interpret input data as constant cells (which are then potentially filtered). This is in contrast to structuredRegular volumes, which have a vertex-centered interpretation.

The VDB implementation in Open VKL follows the following goals:

• Efficient data structure traversal on vector architectures.

• Enable the use of industry-standard .vdb files created through the OpenVDB library.

• Compatibility with OpenVDB on a leaf data level, so that .vdb files may be loaded with minimal overhead.

VDB volumes are created by passing the type string "vdb" to vklNewVolume, and have the following parameters:

Configuration parameters for VDB ("vdb") volumes.#

Type

Name

Default

Description

float[]

indexToObject

1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0

An array of 12 values of type float that define the transformation from index space to object space. In index space, the grid is an axis-aligned regular grid, and leaf voxels have size (1,1,1). The first 9 values are interpreted as a row-major linear transformation matrix. The last 3 values are the translation of the grid origin.

uint32[]

node.format

For each input node, the data format. Currently supported are VKL_FORMAT_TILE for tiles, and VKL_FORMAT_DENSE_ZYX for nodes that are dense regular grids.

uint32[]

node.level

For each input node, the level on which this node exists. Tiles may exist on levels [1, VKL_VDB_NUM_LEVELS-1], all other nodes may only exist on level VKL_VDB_NUM_LEVELS-1.

vec3i[]

node.origin

For each input node, the node origin index.

VKLData[]

node.data

For each input node, the attribute data. Single-attribute volumes may have one array provided per node, while multi-attribute volumes require an array per attribute for each node. Nodes with format VKL_FORMAT_TILE are expected to have single-entry arrays per attribute. Nodes with format VKL_FORMAT_DENSE_ZYX are expected to have arrays with vklVdbLevelNumVoxels(level[i]) entries per attribute. VKL_HALF and VKL_FLOAT data is currently supported; all nodes for a given attribute must be the same data type.

uint32[]

node.temporalFormat

VKL_TEMPORAL_FORMAT_CONSTANT

The temporal format for this volume. Use VKLTemporalFormat for named constants. VDB volumes support VKL_TEMPORAL_FORMAT_CONSTANT, VKL_TEMPORAL_FORMAT_STRUCTURED, and VKL_TEMPORAL_FORMAT_UNSTRUCTURED.

int[]

node.temporallyStructuredNumTimesteps

For temporally structured variation, number of timesteps per voxel. Only valid if temporalFormat is VKL_TEMPORAL_FORMAT_STRUCTURED.

VKLData[]

node.temporallyUnstructuredIndices

For temporally unstructured variation, beginning per voxel. Supported data types for each node are VKL_UINT and VKL_ULONG. Only valid if temporalFormat is VKL_TEMPORAL_FORMAT_UNSTRUCTURED.

VKLData[]

node.temporallyUnstructuredTimes

For temporally unstructured variation, time values corresponding to values in node.data. For each node, the data must be of type VKL_FLOAT. Only valid if temporalFormat is VKL_TEMPORAL_FORMAT_UNSTRUCTURED.

float[]

background

VKL_BACKGROUND_UNDEFINED

For each attribute, the value that is returned when sampling an undefined region outside the volume domain.

The level, origin, format, and data parameters must have the same size, and there must be at least one valid node or commit() will fail.

VDB volumes support temporally structured and temporally unstructured temporal variation. See section ‘Temporal Variation’ for more detail.

The following additional parameters can be set both on vdb volumes and their sampler objects (sampler object parameters default to volume parameters).

Configuration parameters for VDB ("vdb") volumes and their sampler objects.#

Type

Name

Default

Description

int

filter

VKL_FILTER_TRILINEAR

The filter used for reconstructing the field. Use VKLFilter for named constants.

int

filter

The filter used for reconstructing the field during gradient computations. Use VKLFilter for named constants.

int

maxSamplingDepth

VKL_VDB_NUM_LEVELS-1

Do not descend further than to this depth during sampling.

VDB volume objects support the following observers:

Observers supported by VDB ("vdb") volumes.#

Name

Buffer Type

Description

InnerNode

float[]

Return an array of bounding boxes along with value ranges, of inner nodes in the data structure. The bounding box is given in object space. For a volume with M attributes, the entries in this array are (6+2*M)-tuples (minX, minY, minZ, maxX, maxY, maxZ, lower_0, upper_0, lower_1, upper_1, ...). This is in effect a low resolution representation of the volume. The InnerNode observer can be parameterized using int maxDepth to control the depth at which inner nodes are returned. Note that the observer will also return leaf nodes or tiles at lower levels if they exist.

VDB sampler objects support the following observers:

Observers supported by sampler objects created on VDB ("vdb") volumes.#

Name

Buffer Type

Description

LeafNodeAccess

uint32[]

This observer returns an array with as many entries as input nodes were passed. If the input node i was accessed during traversal, then the ith entry in this array has a nonzero value. This can be used for on-demand loading of leaf nodes.

Reconstruction filters#

VDB volumes support the filter types VKL_FILTER_NEAREST, VKL_FILTER_TRILINEAR, and VKL_FILTER_TRICUBIC for both filter and gradientFilter.

Note that when gradientFilter is set to VKL_FILTER_NEAREST, gradients are always $$(0, 0, 0)$$.

Major differences to OpenVDB#

• Open VKL implements sampling in ISPC, and can exploit wide SIMD architectures.

• VDB volumes in Open VKL are read-only once committed, and designed for rendering only. Authoring or manipulating datasets is not in the scope of this implementation.

• The only supported field types are VKL_HALF and VKL_FLOAT at this point. Other field types may be supported in the future. Note that multi-attribute volumes may be used to represent multi-component (e.g. vector) fields.

• The root level in Open VKL has a single node with resolution 64^3 (cp. [1]. OpenVDB uses a hash map, instead).

• Open VKL supports four-level vdb volumes. The resolution of each level can be configured at compile time using CMake variables.

• VKL_VDB_LOG_RESOLUTION_0 sets the base 2 logarithm of the root level resolution. This variable defaults to 6, which means that the root level has a resolution of $$(2^6)^3 = 64^3$$.

• VKL_VDB_LOG_RESOLUTION_1 and VKL_VDB_LOG_RESOLUTION_2 default to 5 and 4, respectively. This matches the default Open VDB resolution for inner levels.

• VKL_VDB_LOG_RESOLUTION_3 set the base 2 logarithm of the leaf level resolution, and defaults to 3. Therefore, leaf nodes have a resolution of $$8^3$$ voxels. Again, this matches the Open VDB default. The default settings lead to a domain resolution of $$2^18^3=262144^3$$ voxels.

Files generated with OpenVDB can be loaded easily since Open VKL vdb volumes implement the same leaf data layout. This means that OpenVDB leaf data pointers can be passed to Open VKL using shared data buffers, avoiding copy operations.

An example of this can be found in utility/vdb/include/openvkl/utility/vdb/OpenVdbGrid.h, where the class OpenVdbFloatGrid encapsulates the necessary operations. This class is also accessible through the vklExamples application using the -file and -field command line arguments.

To use this example feature, compile Open VKL with OpenVDB_ROOT pointing to the OpenVDB prefix.

1. Museth, K. VDB: High-Resolution Sparse Volumes with Dynamic Topology. ACM Transactions on Graphics 32(3), 2013. DOI: 10.1145/2487228.2487235

Particle Volumes#

Particle volumes consist of a set of points in space. Each point has a position, a radius, and a weight typically associated with an attribute. A radial basis function defines the contribution of that particle. Currently, we use the Gaussian radial basis function,

phi(P) = w * exp( -0.5 * ((P - p) / r)^2 )

where P is the particle position, p is the sample position, r is the radius and w is the weight.

At each sample, the scalar field value is then computed as the sum of each radial basis function phi, for each particle that overlaps it. Gradients are similarly computed, based on the summed analytical contributions of each contributing particle.

The Open VKL implementation is similar to direct evaluation of samples in Reda et al.[2]. It uses an Embree-built BVH with a custom traversal, similar to the method in [1].

Particle volumes are created by passing the type string "particle" to vklNewVolume, and have the following parameters:

Configuration parameters for particle ("particle") volumes.#

Type

Name

Default

Description

vec3f[]

particle.position

[data] array of particle positions

float[]

float[]

particle.weight

null

[data] (optional) array of particle weights, specifying the height of the kernel.

float

3.0

The multipler of the particle radius required for support. Larger radii ensure smooth results at the cost of performance. In the Gaussian kernel, the the radius is one standard deviation (sigma), so a radiusSupportFactor of 3 corresponds to 3*sigma.

float

clampMaxCumulativeValue

0

The maximum cumulative value possible, set by user. All cumulative values will be clamped to this, and further traversal (RBF summation) of particle contributions will halt when this value is reached. A value of zero or less turns this off.

bool

estimateValueRanges

true

Enable heuristic estimation of value ranges which are used in internal acceleration structures for interval and hit iterators, as well as for determining the volume’s overall value range. When set to false, the user must specify clampMaxCumulativeValue, and all value ranges will be assumed [0, clampMaxCumulativeValue]. Disabling this may improve volume commit time, but will make interval and hit iteration less efficient.

1. Knoll, A., Wald, I., Navratil, P., Bowen, A., Reda, K., Papka, M.E. and Gaither, K. (2014), RBF Volume Ray Casting on Multicore and Manycore CPUs. Computer Graphics Forum, 33: 71-80. doi:10.1111/cgf.12363

1. Reda, A. Knoll, K. Nomura, M. E. Papka, A. E. Johnson and J. Leigh, “Visualizing large-scale atomistic simulations in ultra-resolution immersive environments,” 2013 IEEE Symposium on Large-Scale Data Analysis and Visualization (LDAV), Atlanta, GA, 2013, pp. 59-65.

Temporal Variation#

Open VKL supports two types of temporal variation: temporally structured and temporally unstructured. When one of these modes is enabled, the volume can be sampled at different times. In both modes, time is assumed to vary between zero and one. This can be useful for implementing renderers with motion blur, for example.

Temporal variation is generally configured through a parameter temporalFormat, which accepts constants from the VKLTemporalFormat enum, though not all modes may be supported by all volumes. On volumes that expect multiple input nodes, the parameter is an array node.temporalFormat, and must provide one value per node. Multiple attributes in a voxel share the same temporal configuration. Please refer to the individual volume sections above to find out supported for each volume type.

temporalFormat defaults to VKL_TEMPORAL_FORMAT_CONSTANT for all volume types. This means that no temporal variation is present in the data.

Temporally structured variation is configured by setting temporalFormat to VKL_TEMPORAL_FORMAT_STRUCTURED. In this mode, the volume expects an additional parameter [node.]temporallyStructuredNumTimesteps, which specifies how many time steps are provided for all voxels, and must be at least 2. A volume, or node, with $$N$$ voxels expects $$N * temporallyStructuredNumTimesteps$$ values for each attribute. The values are assumed evenly spaced over times $$[0, 1]$$: $$\{0, 1/(N-1), ..., 1\}$$

Temporally unstructured variation supports differing time step counts and sample times per voxel. For $$N$$ input voxels, temporallyUnstructuredIndices is an array of $$N+1$$ indices. Voxel $$i$$ has $$N_i = [temporallyUnstructuredIndices[i+1]-temporallyUnstructuredIndices[i])$$ temporal samples starting at index $$temporallyUnstructuredIndices[i]$$. temporallyUnstructuredTimes specifies the times corresponding to the sample values; the time values for each voxel must be between zero and one and strictly increasing: $$t0 < t1 < ... < tN$$. To return a value at sample time t, $$t0 <= t <= tN$$, Open VKL will interpolate linearly from the two nearest time steps. Time values outside this range are clamped to $$[t0, tN]$$.

Sampler Objects#

Computing the value of a volume at an object space coordinate is done using the sampling API, and sampler objects. Sampler objects can be created using

VKLSampler vklNewSampler(VKLVolume volume);


Sampler objects may then be parametrized with traversal parameters. Available parameters are defined by volumes, and are a subset of the volume parameters. As an example, filter can be set on both vdb volumes and their sampler objects. The volume parameter is used as the default for sampler objects. The sampler object parameter provides an override per ray. More detail on parameters can be found in the sections on volumes. Use vklCommit() to commit parameters to the sampler object.

Sampling#

The scalar API takes a volume and coordinate, and returns a float value. The volume’s background value (by default VKL_BACKGROUND_UNDEFINED) is returned for probe points outside the volume. The attribute index selects the scalar attribute of interest; not all volumes support multiple attributes. The time value, which must be between 0 and 1, specifies the sampling time. For temporally constant volumes, this value has no effect.

float vklComputeSample(VKLSampler sampler,
const vkl_vec3f *objectCoordinates,
unsigned int attributeIndex,
float time);


Vector versions allow sampling at 4, 8, or 16 positions at once. Depending on the machine type and Open VKL device implementation, these can give greater performance. An active lane mask valid is passed in as an array of integers; set 0 for lanes to be ignored, -1 for active lanes. An array of time values corresponding to each object coordinate may be provided; a NULL value indicates all times are zero.

void vklComputeSample4(const int *valid,
VKLSampler sampler,
const vkl_vvec3f4 *objectCoordinates,
float *samples,
unsigned int attributeIndex,
const float *times);

void vklComputeSample8(const int *valid,
VKLSampler sampler,
const vkl_vvec3f8 *objectCoordinates,
float *samples,
unsigned int attributeIndex,
const float *times);

void vklComputeSample16(const int *valid,
VKLSampler sampler,
const vkl_vvec3f16 *objectCoordinates,
float *samples,
unsigned int attributeIndex,
const float *times);


A stream version allows sampling an arbitrary number of positions at once. While the vector version requires coordinates to be provided in a structure-of-arrays layout, the stream version allows coordinates to be provided in an array-of-structures layout. Thus, the stream API can be used to avoid reformatting of data by the application. As with the vector versions, the stream API can give greater performance than the scalar API.

void vklComputeSampleN(VKLSampler sampler,
unsigned int N,
const vkl_vec3f *objectCoordinates,
float *samples,
unsigned int attributeIndex,
const float *times);


All of the above sampling APIs can be used, regardless of the device’s native SIMD width.

Sampling Multiple Attributes#

Open VKL provides additional APIs for sampling multiple scalar attributes in a single call through the vklComputeSampleM*() interfaces. Beyond convenience, these can give improved performance relative to the single attribute sampling APIs. As with the single attribute APIs, sampling time values may be specified; note that these are provided per object coordinate only (rather than separately per attribute).

A scalar API supports sampling M attributes specified by attributeIndices on a single object space coordinate:

void vklComputeSampleM(VKLSampler sampler,
const vkl_vec3f *objectCoordinates,
float *samples,
unsigned int M,
const unsigned int *attributeIndices,
float time);


Vector versions allow sampling at 4, 8, or 16 positions at once across the M attributes:

void vklComputeSampleM4(const int *valid,
VKLSampler sampler,
const vkl_vvec3f4 *objectCoordinates,
float *samples,
unsigned int M,
const unsigned int *attributeIndices,
const float *times);

void vklComputeSampleM8(const int *valid,
VKLSampler sampler,
const vkl_vvec3f8 *objectCoordinates,
float *samples,
unsigned int M,
const unsigned int *attributeIndices,
const float *times);

void vklComputeSampleM16(const int *valid,
VKLSampler sampler,
const vkl_vvec3f16 *objectCoordinates,
float *samples,
unsigned int M,
const unsigned int *attributeIndices,
const float *times);


The [4, 8, 16] * M sampled values are populated in the samples array in a structure-of-arrays layout, with all values for each attribute provided in sequence. That is, sample values s_m,n for the mth attribute and nth object coordinate will be populated as

samples = [s_0,0,   s_0,1,   ..., s_0,N-1,
s_1,0,   s_1,1,   ..., s_1,N-1,
...,
s_M-1,0, s_M-1,1, ..., s_M-1,N-1]


A stream version allows sampling an arbitrary number of positions at once across the M attributes. As with single attribute stream sampling, the N coordinates are provided in an array-of-structures layout.

void vklComputeSampleMN(VKLSampler sampler,
unsigned int N,
const vkl_vec3f *objectCoordinates,
float *samples,
unsigned int M,
const unsigned int *attributeIndices,
const float *times);


The M * N sampled values are populated in the samples array in an array-of-structures layout, with all attribute values for each coordinate provided in sequence as

samples = [s_0,0,   s_1,0,   ..., s_M-1,0,
s_0,1,   s_1,1,   ..., s_M-1,1,
...,
s_0,N-1, s_1,N-1, ..., s_M-1,N-1]


All of the above sampling APIs can be used, regardless of the device’s native SIMD width.

In a very similar API to vklComputeSample, vklComputeGradient queries the value gradient at an object space coordinate. Again, a scalar API, now returning a vec3f instead of a float. NaN values are returned for points outside the volume. The time value, which must be between 0 and 1, specifies the sampling time. For temporally constant volumes, this value has no effect.

vkl_vec3f vklComputeGradient(VKLSampler sampler,
const vkl_vec3f *objectCoordinates,
unsigned int attributeIndex,
float time);


Vector versions are also provided:

void vklComputeGradient4(const int *valid,
VKLSampler sampler,
const vkl_vvec3f4 *objectCoordinates,
unsigned int attributeIndex,
const float *times);

VKLSampler sampler,
const vkl_vvec3f8 *objectCoordinates,
unsigned int attributeIndex,
const float *times);

VKLSampler sampler,
const vkl_vvec3f16 *objectCoordinates,
unsigned int attributeIndex,
const float *times);


Finally, a stream version is provided:

void vklComputeGradientN(VKLSampler sampler,
unsigned int N,
const vkl_vec3f *objectCoordinates,
unsigned int attributeIndex,
const float *times);


All of the above gradient APIs can be used, regardless of the device’s native SIMD width.

Iterators#

Open VKL has APIs to search for particular volume values along a ray. Queries can be for ranges of volume values (vklIterateInterval) or for particular values (vklIterateHit).

Interval iterators require a context object to define the sampler and parameters related to iteration behavior. An interval iterator context is created via

VKLIntervalIteratorContext vklNewIntervalIteratorContext(VKLSampler sampler);


The parameters understood by interval iterator contexts are defined in the table below.

Configuration parameters for interval iterator contexts.#

Type

Name

Default

Description

int

attributeIndex

0

Defines the volume attribute of interest.

vkl_range1f[]

valueRanges

[-inf, inf]

Defines the value ranges of interest. Intervals not containing any of these values ranges may be skipped during iteration.

float

intervalResolutionHint

0.5

A value in the range [0, 1] affecting the resolution (size) of returned intervals. A value of 0 yields the lowest resolution (largest) intervals while 1 gives the highest resolution (smallest) intervals. This value is only a hint; it may not impact behavior for all volume types.

Most volume types support the intervalResolutionHint parameter that can impact the size of intervals returned duration iteration. These include amr, particle, structuredRegular, unstructured, and vdb volumes. In all cases a value of 1.0 yields the highest resolution (smallest) intervals possible, while a value of 0.0 gives the lowest resolution (largest) intervals. In general, smaller intervals will have tighter bounds on value ranges, and more efficient space skipping behavior than larger intervals, which can be beneficial for some rendering methods.

For structuredRegular, unstructured, and vdb volumes, a value of 1.0 will enable elementary cell iteration, such that each interval spans an individual voxel / cell intersection. Note that interval iteration can be significantly slower in this case.

As with other objects, the interval iterator context must be committed before being used.

To query an interval, a VKLIntervalIterator of scalar or vector width must be initialized with vklInitIntervalIterator. Time value(s) may be provided to specify the sampling time. These values must be between 0 and 1; for the vector versions, a NULL value indicates all times are zero. For temporally constant volumes, the time values have no effect.

VKLIntervalIterator vklInitIntervalIterator(VKLIntervalIteratorContext context,
const vkl_vec3f *origin,
const vkl_vec3f *direction,
const vkl_range1f *tRange,
float time,
void *buffer);

VKLIntervalIterator4 vklInitIntervalIterator4(const int *valid,
VKLIntervalIteratorContext context,
const vkl_vvec3f4 *origin,
const vkl_vvec3f4 *direction,
const vkl_vrange1f4 *tRange,
const float *times,
void *buffer);

VKLIntervalIterator8 vklInitIntervalIterator8(const int *valid,
VKLIntervalIteratorContext context,
const vkl_vvec3f8 *origin,
const vkl_vvec3f8 *direction,
const vkl_vrange1f8 *tRange,
const float *times,
void *buffer);

VKLIntervalIterator16 vklInitIntervalIterator16(const int *valid,
VKLIntervalIteratorContext context,
const vkl_vvec3f16 *origin,
const vkl_vvec3f16 *direction,
const vkl_vrange1f16 *tRange,
const float *times,
void *buffer);


Open VKL places the iterator struct into a user-provided buffer, and the returned handle is essentially a pointer into this buffer. This means that the iterator handle must not be used after the buffer ceases to exist. Copying iterator buffers is currently not supported.

The required size, in bytes, of the buffer can be queried with

size_t vklGetIntervalIteratorSize(VKLIntervalIteratorContext context);

size_t vklGetIntervalIteratorSize4(VKLIntervalIteratorContext context);

size_t vklGetIntervalIteratorSize8(VKLIntervalIteratorContext context);

size_t vklGetIntervalIteratorSize16(VKLIntervalIteratorContext context);


The values these functions return may change depending on the parameters set on sampler.

Open VKL also provides a conservative maximum size over all volume types as a preprocessor definition (VKL_MAX_INTERVAL_ITERATOR_SIZE). For ISPC use cases, Open VKL will attempt to detect the native vector width using TARGET_WIDTH, which is defined in recent versions of ISPC, to provide a less conservative size.

Intervals can then be processed by calling vklIterateInterval as long as the returned lane masks indicates that the iterator is still within the volume:

int vklIterateInterval(VKLIntervalIterator iterator,
VKLInterval *interval);

void vklIterateInterval4(const int *valid,
VKLIntervalIterator4 iterator,
VKLInterval4 *interval,
int *result);

void vklIterateInterval8(const int *valid,
VKLIntervalIterator8 iterator,
VKLInterval8 *interval,
int *result);

void vklIterateInterval16(const int *valid,
VKLIntervalIterator16 iterator,
VKLInterval16 *interval,
int *result);


The intervals returned have a t-value range, a value range, and a nominalDeltaT which is approximately the step size (in units of ray direction) that should be used to walk through the interval, if desired. The number and length of intervals returned is volume type implementation dependent. There is currently no way of requesting a particular splitting.

typedef struct
{
vkl_range1f tRange;
vkl_range1f valueRange;
float nominalDeltaT;
} VKLInterval;

typedef struct
{
vkl_vrange1f4 tRange;
vkl_vrange1f4 valueRange;
float nominalDeltaT[4];
} VKLInterval4;

typedef struct
{
vkl_vrange1f8 tRange;
vkl_vrange1f8 valueRange;
float nominalDeltaT[8];
} VKLInterval8;

typedef struct
{
vkl_vrange1f16 tRange;
vkl_vrange1f16 valueRange;
float nominalDeltaT[16];
} VKLInterval16;


Querying for particular values is done using a VKLHitIterator in much the same fashion. This API could be used, for example, to find isosurfaces. As with interval iterators, time value(s) may be provided to specify the sampling time. These values must be between 0 and 1; for the vector versions, a NULL value indicates all times are zero. For temporally constant volumes, the time values have no effect.

Hit iterators similarly require a context object to define the sampler and other iteration parameters. A hit iterator context is created via

VKLHitIteratorContext vklNewHitIteratorContext(VKLSampler sampler);


The parameters understood by hit iterator contexts are defined in the table below.

Configuration parameters for hit iterator contexts.#

Type

Name

Default

Description

int

attributeIndex

0

Defines the volume attribute of interest.

float[]

values

Defines the value(s) of interest.

The hit iterator context must be committed before being used.

Again, a user allocated buffer must be provided, and a VKLHitIterator of the desired width must be initialized:

VKLHitIterator vklInitHitIterator(VKLHitIteratorContext context,
const vkl_vec3f *origin,
const vkl_vec3f *direction,
const vkl_range1f *tRange,
float time,
void *buffer);

VKLHitIterator4 vklInitHitIterator4(const int *valid,
VKLHitIteratorContext context,
const vkl_vvec3f4 *origin,
const vkl_vvec3f4 *direction,
const vkl_vrange1f4 *tRange,
const float *times,
void *buffer);

VKLHitIterator8 vklInitHitIterator8(const int *valid,
VKLHitIteratorContext context,
const vkl_vvec3f8 *origin,
const vkl_vvec3f8 *direction,
const vkl_vrange1f8 *tRange,
const float *times,
void *buffer);

VKLHitIterator16 vklInitHitIterator16(const int *valid,
VKLHitIteratorContext context,
const vkl_vvec3f16 *origin,
const vkl_vvec3f16 *direction,
const vkl_vrange1f16 *tRange,
const float *times,
void *buffer);


Buffer size can be queried with

size_t vklGetHitIteratorSize(VKLHitIteratorContext context);

size_t vklGetHitIteratorSize4(VKLHitIteratorContext context);

size_t vklGetHitIteratorSize8(VKLHitIteratorContext context);

size_t vklGetHitIteratorSize16(VKLHitIteratorContext context);


Open VKL also provides the macro VKL_MAX_HIT_ITERATOR_SIZE as a conservative estimate.

Hits are then queried by looping a call to vklIterateHit as long as the returned lane mask indicates that the iterator is still within the volume.

int vklIterateHit(VKLHitIterator iterator, VKLHit *hit);

void vklIterateHit4(const int *valid,
VKLHitIterator4 iterator,
VKLHit4 *hit,
int *result);

void vklIterateHit8(const int *valid,
VKLHitIterator8 iterator,
VKLHit8 *hit,
int *result);

void vklIterateHit16(const int *valid,
VKLHitIterator16 iterator,
VKLHit16 *hit,
int *result);


Returned hits consist of a t-value, a volume value (equal to one of the requested values specified in the context), and an (object space) epsilon value estimating the error of the intersection:

typedef struct
{
float t;
float sample;
float epsilon;
} VKLHit;

typedef struct
{
float t[4];
float sample[4];
float epsilon[4];
} VKLHit4;

typedef struct
{
float t[8];
float sample[8];
float epsilon[8];
} VKLHit8;

typedef struct
{
float t[16];
float sample[16];
float epsilon[16];
} VKLHit16;


For both interval and hit iterators, only the vector-wide API for the native SIMD width (determined via vklGetNativeSIMDWidth can be called. The scalar versions are always valid. This restriction will likely be lifted in the future.

Performance Recommendations#

MXCSR control and status register#

It is strongly recommended to have the Flush to Zero and Denormals are Zero mode of the MXCSR control and status register enabled for each thread before calling the sampling, gradient, or interval API functions. Otherwise, under some circumstances special handling of denormalized floating point numbers can significantly reduce application and Open VKL performance. The device parameter flushDenormals or environment variable OPENVKL_FLUSH_DENORMALS can be used to toggle this mode; by default it is enabled. Alternatively, when using Open VKL together with the Intel® Threading Building Blocks, it is sufficient to execute the following code at the beginning of the application main thread (before the creation of the tbb::task_scheduler_init object):

#include <xmmintrin.h>
#include <pmmintrin.h>
...
_MM_SET_FLUSH_ZERO_MODE(_MM_FLUSH_ZERO_ON);
_MM_SET_DENORMALS_ZERO_MODE(_MM_DENORMALS_ZERO_ON);


If using a different tasking system, make sure each thread calling into Open VKL has the proper mode set.

Iterator Allocation#

vklInitIntervalIterator and vklInitHitIterator expect a user allocated buffer. While this buffer can be allocated by any means, we expect iterators to be used in inner loops and advise against heap allocation in that case. Applications may provide high performance memory pools, but as a preferred alternative we recommend stack allocated buffers.

In C99, variable length arrays provide an easy way to achieve this:

const size_t bufferSize = vklGetIntervalIteratorSize(sampler);
char buffer[bufferSize];


Note that the call to vklGetIntervalIteratorSize or vklGetHitIteratorSize should not appear in an inner loop as it is relatively costly. The return value depends on the volume type, target architecture, and parameters to sampler.

In C++, variable length arrays are not part of the standard. Here, users may rely on alloca and similar functions:

#include <alloca.h>
const size_t bufferSize = vklGetIntervalIteratorSize(sampler);
void *buffer = alloca(bufferSize);


Similarly for ISPC, variable length arrays are not supported, but alloca may be used:

const uniform size_t bufferSize = vklGetIntervalIteratorSizeV(sampler);
void *uniform buffer = alloca(bufferSize);


Users should understand the implications of alloca. In particular, alloca does check available stack space and may result in stack overflow. buffer also becomes invalid at the end of the scope. As one consequence, it cannot be returned from a function. On Windows, _malloca is a safer option that performs additional error checking, but requires the use of _freea.

Applications may instead rely on the VKL_MAX_INTERVAL_ITERATOR_SIZE and VKL_MAX_HIT_ITERATOR_SIZE macros. For example, in ISPC:

uniform unsigned int8 buffer[VKL_MAX_INTERVAL_ITERATOR_SIZE];


These values are majorants over all devices and volume types. Note that Open VKL attempts to detect the target SIMD width using TARGET_WIDTH, returning smaller buffer sizes for narrow architectures. However, Open VKL may fall back to the largest buffer size over all targets.

Multi-attribute Volume Data Layout#

Open VKL provides flexible managed data APIs that allow applications to specify input data in various formats and layouts. When shared buffers are used (dataCreationFlags = VKL_DATA_SHARED_BUFFER), Open VKL will use the application-owned memory directly, respecting the input data layout. Shared buffers therefore allow applications to strategically select the best layout for multi-attribute volume data and expected sampling behavior.

For volume attributes that are sampled individually (e.g. using vklComputeSample[4,8,16,N]()), it is recommended to use a structure-of-arrays layout. That is, each attribute’s data should be compact in contiguous memory. This can be accomplished by simply using Open VKL owned data objects (dataCreationFlags = VKL_DATA_DEFAULT), or by using a natural byteStride for shared buffers.

For volume attributes that are sampled simultaneously (e.g. using vklComputeSampleM[4,8,16,N]()), it is recommended to use an array-of-structures layout. That is, data for these attributes should be provided per voxel in a contiguous layout. This is accomplished using shared buffers for each attribute with appropriate byte strides. For example, for a three attribute structured volume representing a velocity field, the data can be provided as:

// used in Open VKL shared buffers, so must not be freed by application
std::vector<vkl_vec3f> velocities(numVoxels);

for (auto &v : velocities) {
v.x = ...;
v.y = ...;
v.z = ...;
}

std::vector<VKLData> attributes;

attributes.push_back(vklNewData(device,
velocities.size(),
VKL_FLOAT,
&velocities[0].x,
VKL_DATA_SHARED_BUFFER,
sizeof(vkl_vec3f)));

attributes.push_back(vklNewData(device,
velocities.size(),
VKL_FLOAT,
&velocities[0].y,
VKL_DATA_SHARED_BUFFER,
sizeof(vkl_vec3f)));

attributes.push_back(vklNewData(device,
velocities.size(),
VKL_FLOAT,
&velocities[0].z,
VKL_DATA_SHARED_BUFFER,
sizeof(vkl_vec3f)));

VKLData attributesData =
vklNewData(device, attributes.size(), VKL_DATA, attributes.data());

for (auto &attribute : attributes)
vklRelease(attribute);

VKLVolume volume = vklNewVolume(device, "structuredRegular");

vklSetData(volume, "data", attributesData);
vklRelease(attributesData);

// set other volume parameters...

vklCommit(volume);


These are general recommendations for common scenarios; it is still recommended to evaluate performance of different volume data layouts for your application’s particular use case.