This section is intended for troubleshooting problems with zfp, in case any arise, and primarily focuses on how to correctly make use of zfp. If the decompressed data looks nothing like the original data, or if the compression ratios obtained seem not so impressive, then it is very likely that array dimensions or compression parameters have not been set correctly, in which case this troubleshooting guide could help.

The problems addressed in this section include:

P1: Is the data dimensionality correct?

This is one of the most common problems. First, make sure that zfp is given the correct dimensionality of the data. For instance, an audio stream is a 1D array, an image is a 2D array, and a volume grid is a 3D array, and a time-varying volume is a 4D array. Sometimes a data set is a discrete collection of lower-dimensional objects. For instance, a stack of unrelated images (of the same size) could be represented in C as a 3D array:


but since in this case the images are unrelated, no correlation would be expected along the third dimension—the underlying dimensionality of the data is here two. In this case, the images could be compressed one at a time, or they could be compressed together by treating the array dimensions as:

imstack[count * ny][nx]

Note that zfp partitions d-dimensional arrays into blocks of 4d values. If ny above is not a multiple of four, then some blocks of 4 × 4 pixels will contain pixels from different images, which could hurt compression and/or quality. Still, this way of creating a single image by stacking multiple images is far preferable over linearizing each image into a 1D signal, and then compressing the images as:

imstack[count][ny * nx]

This loses the correlation along the y dimension and further introduces discontinuities unless nx is a multiple of four.

Similarly to the example above, a 2D vector field


could be declared as a 3D array, but the x- and y-components of the 2D vectors are likely entirely unrelated. In this case, each component needs to be compressed independently, either by rearranging the data as two scalar fields:


or by using strides (see also FAQ #1). Note that in all these cases zfp will still compress the data, but if the dimensionality is not correct then the compression ratio will suffer.

P2: Do the compressor and decompressor agree on the dimensionality?

Consider compressing a 3D array:

double a[1][1][100]

with nx = 100, ny = 1, nz = 1, then decompressing the result to a 1D array:

double b[100]

with nx = 100. Although the arrays a and b occupy the same amount of memory and are in C laid out similarly, these arrays are not equivalent to zfp because their dimensionalities differ. zfp uses different CODECs to (de)compress 1D, 2D, 3D, and 4D arrays, and the 1D decompressor expects a compressed bit stream that corresponds to a 1D array.

What happens in practice in this case is that the array a is compressed using zfp’s 3D CODEC, which first pads the array to

double padded[4][4][100]

When this array is correctly decompressed using the 3D CODEC, the padded values are generated but discarded. zfp’s 1D decompressor, on the other hand, expects 100 values, not 100 × 4 × 4 = 1600 values, and therefore likely returns garbage.

P3: Have the “smooth” dimensions been identified?

Closely related to P1 above, some fields simply do not vary smoothly along all dimensions, and zfp can do a good job compressing only those dimensions that exhibit some coherence. For instance, consider a table of stock prices indexed by date and stock:


One could be tempted to compress this as a 2D array, but there is likely little to no correlation in prices between different stocks. Each such time series should be compressed independently as a 1D signal.

What about time-varying images like a video sequence? In this case, it is likely that there is correlation over time, and that the value of a single pixel varies smoothly in time. It is also likely that each image exhibits smoothness along its two spatial dimensions. So this can be treated as a single, 3D data set.

How about time-varying volumes, such as


As of version 0.5.4, zfp supports compression of 4D arrays. Since all dimensions in this example are likely to be correlated, the 4D array can be compressed directly. Alternatively, the data could be organized by the three “smoothest” dimensions and compressed as a 3D array. Given the organization above, the array could be treated as 3D:

field[nt * nz][ny][nx]

Again, do not compress this as a 3D array with the innermost dimensions unfolded:

field[nt][nz][ny * nx]

P4: Are the array dimensions correct?

This is another common problem that seems obvious, but often the dimensions are accidentally transposed. Assuming that the smooth dimensions have been identified, it is important that the dimensions are listed in the correct order. For instance, if the data (in C notation) is organized as:


then the data is organized in memory (or on disk) with the d3 dimension varying fastest, and hence nx = d3, ny = d2, nz = d1 using the zfp naming conventions for the dimensions, e.g., the zfp executable should be invoked with:

zfp -3 d3 d2 d1

in this case. Things will go horribly wrong if zfp in this case is called with nx = d1, ny = d2, nz = d3. The entire data set will still compress and decompress, but compression ratio and quality will likely suffer greatly. See this FAQ for more details.

P5: Are the array dimensions large enough?

zfp partitions d-dimensional data sets into blocks of 4d values, e.g., in 3D a block consists of 4 × 4 × 4 values. If the dimensions are not multiples of four, then zfp will “pad” the array to the next larger multiple of four. Such padding can hurt compression. In particular, if one or more of the array dimensions are small, then the overhead of such padding could be significant.

Consider compressing a collection of 1000 small 3D arrays:


zfp would first logically pad this to a larger array:


which is (8 × 16 × 4) / (5 × 14 × 2) ~ 3.66 times larger. Although such padding often compresses well, this still represents a significant overhead.

If a large array has been partitioned into smaller pieces, it may be best to reassemble the larger array. Or, when possible, ensure that the sub-arrays have dimensions that are multiples of four.

P6: Is the data logically structured?

zfp was designed for logically structured data, i.e., Cartesian grids. It works much like an image compressor does, which assumes that the data set is a structured array of pixels, and it assumes that values vary reasonably smoothly on average, just like natural images tend to contain large regions of uniform color or smooth color gradients, like a blue sky, smoothly varying skin tones of a human’s face, etc. Many data sets are not represented on a regular grid. For instance, an array of particle xyz positions:


is a 2D array, but does not vary smoothly in either dimension. Furthermore, such unstructured data sets need not be organized in any particular order; the particles could be listed in any arbitrary order. One could attempt to sort the particles, for example by the x coordinate, to promote smoothness, but this would still leave the other two dimensions non-smooth.

Sometimes the underlying dimensions are not even known, and only the total number of floating-point values is known. For example, suppose we only knew that the data set contained n = count × 3 values. One might be tempted to compress this using zfp’s 1-dimensional compressor, but once again this would not work well. Such abuse of zfp is much akin to trying to compress an image using an audio compressor like mp3, or like compressing an n-sample piece of music as an n-by-one sized image using an image compressor like JPEG. The results would likely not be very good.

Some data sets are logically structured but geometrically irregular. Examples include fields stored on Lagrangian meshes that have been warped, or on spectral element grids, which use a non-uniform grid spacing. zfp assumes that the data has been regularly sampled in each dimension, and the more the geometry of the sampling deviates from uniform, the worse compression gets. Note that rectilinear grids with different but uniform grid spacing in each dimension are fine. If your application uses very non-uniform sampling, then resampling onto a uniform grid (if possible) may be advisable.

Other data sets are “block structured” and consist of piecewise structured grids that are “glued” together. Rather than treating such data as unstructured 1D streams, consider partitioning the data set into independent (possibly overlapping) regular grids.

P7: Is the data set embedded in a regular grid?

Some applications represent irregular geometry on a Cartesian grid, and leave portions of the domain unspecified. Consider, for instance, sampling the density of the Earth onto a Cartesian grid. Here the density for grid points outside the Earth is unspecified.

In this case, zfp does best by initializing the “background field” to all zeros. In zfp’s fixed-accuracy mode, any “empty” block that consists of all zeros is represented using a single bit, and therefore the overhead of representing empty space can be kept low.

P8: Have fill values, NaNs, and infinities been removed?

It is common to signal unspecified values using what is commonly called a “fill value,” which is a special constant value that tends to be far out of range of normal values. For instance, in climate modeling the ocean temperature over land is meaningless, and it is common to use a very large temperature value such as 1e30 to signal that the temperature is undefined for such grid points.

Very large fill values do not play well with zfp, because they both introduce artificial discontinuities and pollute nearby values by expressing them all with respect to the common largest exponent within their block. Assuming a fill value of 1e30, the value pi in the same block would be represented as:

0.00000000000000000000000000000314159... * 1e30

Given finite precision, the small fraction would likely be replaced with zero, resulting in complete loss of the actual value being stored.

Other applications use NaNs (special not-a-number values) or infinities as fill values. These are even more problematic, because they do not have a defined exponent. zfp relies on the C function frexp() to compute the exponent of the largest (in magnitude) value within a block, but produces unspecified behavior if that value is not finite.

zfp currently has no independent mechanism for handling fill values. Ideally such special values would be signalled separately, e.g., using a bit mask, and then replaced with zeros to ensure that they both compress well and do not pollute actual data.

P9: Is the byte order correct?

zfp generally works with the native byte order (e.g., little or big endian) of the machine it is compiled on. One needs only be concerned with byte order when reading raw, binary data into the zfp executable, when exchanging compressed files across platforms, and when varying the bit stream word size on big endian machines (not common). For instance, to compress a binary double-precision floating-point file stored in big endian byte order on a little endian machine, byte swapping must first be done. For example, on Linux and macOS, 8-byte doubles can be byte swapped using:

objcopy -I binary -O binary --reverse-bytes=8 big.bin little.bin

See also FAQ #11 for more discussion of byte order.

P10: Is the floating-point precision correct?

Another obvious problem: Please make sure that zfp is told whether the data to compress is an array of single- (32-bit) or double-precision (64-bit) values, e.g., by specifying the -f or -d options to the zfp executable or by passing the appropriate zfp_type to the C functions.

P11: Is the integer precision correct?

zfp currently supports compression of 31- or 63-bit signed integers. Shorter integers (e.g., bytes, shorts) can be compressed but must first be promoted to one of the longer types. This should always be done using zfp’s functions for promotion and demotion, which both perform bit shifting and biasing to handle both signed and unsigned types. It is not sufficient to simply cast short integers to longer integers. See also FAQs #8 and #9.

P12: Is the data provided to the zfp executable a raw binary array?

zfp expects that the input file is a raw binary array of integers or floating-point values in the IEEE format, e.g., written to file using fwrite(). Do not hand zfp a text file containing ASCII floating-point numbers. Strip the file of any header information. Languages like Fortran tend to store with the array its size. No such metadata may be embedded in the file.

P13: Has the appropriate compression mode been set?

zfp provides three different lossy modes of compression that trade storage and accuracy, plus one lossless mode. In fixed-rate mode, the user specifies the exact number of bits (often in increments of a fraction of a bit) of compressed storage per value (but see FAQ #18 for caveats). From the user’s perspective, this seems a very desirable feature, since it provides for a direct mechanism for specifying how much storage to use. However, there is often a large quality penalty associated with the fixed-rate mode, because each block of 4d values is allocated the same number of bits. In practice, the information content over the data set varies significantly, which means that easy-to-compress regions are assigned too many bits, while too few bits are available to faithfully represent the more challenging-to-compress regions. Although one of the unique features of zfp, its fixed-rate mode should primarily be used only when random access to the data is needed.

zfp also provides a fixed-precision mode, where the user specifies how many uncompressed significant bits to use to represent the floating-point fraction. This precision may not be exactly what people might normally think of. For instance, the C float type is commonly referred to as 32-bit precision. However, the sign bit and exponent account for nine of those bits and do not contribute to the number of significant bits of precision. Furthermore, for normal numbers, IEEE uses a hidden implicit one bit, so most float values actually have 24 bits of precision. Furthermore, zfp uses a block-floating-point representation with a single exponent per block, which may cause some small values to have several leading zero bits and therefore less precision than requested. Thus, the effective precision returned by zfp in its fixed-precision mode may in fact vary. In practice, the precision requested is only an upper bound, though typically at least one value within a block has the requested precision.

zfp supports a fixed-accuracy mode, which except in rare circumstances (see FAQ #17) ensures that the absolute error is bounded, i.e., the difference between any decompressed and original value is at most the tolerance specified by the user (but usually several times smaller). Whenever possible, we recommend using this compression mode, which depending on how easy the data is to compress results in the smallest compressed stream that respects the error tolerance.

As of zfp 0.5.5, reversible (lossless) compression is available. The amount of lossless reduction of floating-point data is usually quite limited, however, especially for double-precision data. Unless a bit-for-bit exact reconstruction is needed, we strongly advocate the use of lossy compression.

Finally, there is also an expert mode that allows the user to combine the constraints of fixed rate, precision, and accuracy. See the section on compression modes for more details.