README > CUTLASS Profiler
The CUTLASS Profiler is a command-line driven test and profiling environment for CUTLASS computations defined in the CUTLASS Instance Library. The CUTLASS Profiler is capable of executing each GEMM, Sparse Gemm, Conv2d, and Conv3d kernel.
The CUTLASS Profiler may be compiled with:
$ make cutlass_profiler -jTo limit compilation time, only one tile size (typically 128x128) and threadblock cluster size (typically 2x1x1) is instantiated for each data type,
math instruction, and layout. To instantiate all sizes, set the following environment variable when running CMake from an
empty build/ directory.
$ cmake .. -DCUTLASS_NVCC_ARCHS="70;75;80" -DCUTLASS_LIBRARY_KERNELS=all -DCUTLASS_UNITY_BUILD_ENABLED=ON
...
$ make cutlass_profiler -jEnabling the unity build places multiple kernel instances in one compilation unit, thereby reducing size of the compiled binary and avoiding linker limitations on some platforms.
The CUTLASS Profiler sources are stored in
tools/
profiler/The CUTLASS Profiler usage statement may be obtained by executing cutlass_profiler --help and appears as follows.
CUTLASS Performance Tool
usage:
cutlass_profiler [options]
--help
--mode=<string> Cutlass profiler execution mode.
--mode=profile regular verification and profiling (default)
--mode=dry_run no kernels are launched or workspaces allocated
--mode=enumerate lists all operation kind and operations
--mode=trace executes a single device-side computation with
no other kernel launches
--device-info Prints information on all GPUs present in the system
--operation=<operation_kind> CUTLASS operation to profile.
--kernels=<string_list> Filter operations by kernel names. For example, call all kernels with
("s1688" and "nt") or ("s844" and "tn" and "align8") in their
operation name using --kernels="s1688*nt, s884*tn*align8"
--ignore-kernels=<string_list> Excludes kernels whose names match anything in this list.
Device:
--device=<int> CUDA Device ID
--compute-capability=<int> Override the compute capability.
--llc-capacity=<capacity in KiB> Capacity of last-level cache in kilobytes. If this is non-zero,
profiling phases cycle through different input tensors to induce
capacity misses in the L2.
Initialization:
--initialization=<bool> Enables initialization (default: true). If false, device memory is
not initialized after allocation.
--initialization-provider=<provider> Selects initialization provider {host, device*}. (default: '*')
--dist=<distribution> Data distribution of input tensors {uniform*, gaussian, identity, sequential}
--dist=uniform,min:<double>,max:<double>,scale:<integer>
--dist=gaussian,mean:<double>,stddev:<double>,scale:<integer>
--dist=sequential,start:<double>,delta:<double>,scale:<integer>
--dist=identity
--seed=<int> Random number generator seed. Used to enforce deterministic
initialization.
Library:
--library-algo-mode=<mode> Indicates algorithm mode used to call libraries such as cuBLAS and cuDNN.
mode={default*,matching,best}
--library-algos=<range-list> If --algorithm-mode=best, permits specifying a selection of algorithms.
Profiling:
--workspace-count=<workspace count> Number of discrete workspaces maintained to avoid cache-resident
If zero (default), the amount is chosen for each workload based on
capacity of the last-level cache.
--profiling-iterations=<iterations> Number of iterations to profile each kernel. If zero, kernels
are launched up to the profiling duration.
--warmup-iterations=<iterations> Number of iterations to execute each kernel prior to profiling.
--sleep-duration=<duration> Number of ms to sleep between profiling periods (ms).
--profiling-enabled=<bool> If true, profiling is actually conducted.
Verification:
--verification-enabled=<bool> Whether to perform verification checks.
--epsilon=<error> Error threshold. Setting to zero (default) requires
bit-level equivalence.
--nonzero-floor=<floor> Results whose absolute value is less than this quantity
are treated as zero for comparisons.
--save-workspace=<string> Specifies when to save the GEMM inputs and results to the filesystem.
--save-workspace=never never save workspace (default)
--save-workspace=incorrect save workspace for incorrect results
--save-workspace=always always save workspace
--verification-providers=<providers> List of providers used to verify result. (default: '*')
Gemm verification-providers {cublas*}
Conv2d verification-providers {cudnn*, device*, host}
Report:
--append=<bool> If true, result is appended to possibly existing file. Otherwise,
any existing file is overwritten.
--output=<path> Path to output file for machine readable results. Operation kind and '.csv' is appended.
--junit-output=<path> Path to junit output file for result reporting. Operation kind and '.junit.xml' is appended.
--report-not-run=<bool> If true, reports the status of all kernels including those that
do not satisfy the given arguments.
--tags=<column:tag,...> Inserts leading columns in output table and uniform values for each
column. Useful for generating pivot tables.
--verbose=<bool> Prints human-readable text to stdout. If false, nothing is written to stdout.
About:
--version CUTLASS 2.4.0 built on Nov 19 2020 at 11:59:00
Operations:
gemm General matrix-matrix product. D = alpha * A*B + beta * C
spgemm Structured sparse GEMM. D = alpha * A*B + beta * C
conv2d Conv2d operation. Output(Tensor4D) = alpha * Input(Tensor4D) * Filter(Tensor4D) + beta * Input(Tensor4D)
conv3d Conv3d operation. Output(Tensor5D) = alpha * Input(Tensor5D) * Filter(Tensor5D) + beta * Input(Tensor5D)
For details about a particular function, specify the function name with --help.
Example:
$ cutlass_profiler --operation=Gemm --help
$ cutlass_profiler --operation=Conv3d --help
$ cutlass_profiler --operation=Conv2d --help
The CUTLASS Profiler is capable of executing GEMM and Sparse GEMM problems.
The CUTLASS Profiler can be built with cuBLAS enabled to use as a reference implementation. If CMake detects
the cuBLAS library available in the system, it is included as a dependency. This may be explicitly overridden
with CMake flag CUTLASS_ENABLE_CUBLAS.
The complete set of arguments available to each operation may be viewed by specifying the operation name
in addition to --help. The argument flags and their aliases usable for GEMM appear as follows.
$ ./tools/profiler/cutlass_profiler --operation=gemm --help
GEMM
[enum] --gemm_kind Variant of GEMM (e.g. universal, gemm, planar_complex, planar_complex_array)
[int] --m,--problem-size::m M dimension of the GEMM problem space
[int] --n,--problem-size::n N dimension of the GEMM problem space
[int] --k,--problem-size::k K dimension of the GEMM problem space
[tensor] --A Tensor storing the A operand
[tensor] --B Tensor storing the B operand
[tensor] --C Tensor storing the C operand
[scalar] --alpha,--epilogue::alpha Epilogue scalar alpha
[scalar] --beta,--epilogue::beta Epilogue scalar beta
[enum] --split_k_mode,--split-k-mode Variant of split K mode(serial, parallel)
[int] --split_k_slices,--split-k-slices Number of partitions of K dimension
[int] --batch_count,--batch-count Number of GEMMs computed in one batch
[enum] --op_class,--opcode-class Class of math instruction (simt, tensorop, wmmatensorop, wmma).
[enum] --accum,--accumulator-type Math instruction accumulator data type
[int] --cta_m,--threadblock-shape::m Threadblock shape in the M dimension
[int] --cta_n,--threadblock-shape::n Threadblock shape in the N dimension
[int] --cta_k,--threadblock-shape::k Threadblock shape in the K dimension
[int] --cluster_m,--cluster-shape::m Cluster shape in the M dimension
[int] --cluster_n,--cluster-shape::n Cluster shape in the N dimension
[int] --cluster_k,--cluster-shape::k Cluster shape in the K dimension
[int] --stages,--threadblock-stages Number of stages of threadblock-scoped matrix multiply
[int] --warps_m,--warp-count::m Number of warps within threadblock along the M dimension
[int] --warps_n,--warp-count::n Number of warps within threadblock along the N dimension
[int] --warps_k,--warp-count::k Number of warps within threadblock along the K dimension
[int] --inst_m,--instruction-shape::m Math instruction shape in the M dimension
[int] --inst_n,--instruction-shape::n Math instruction shape in the N dimension
[int] --inst_k,--instruction-shape::k Math instruction shape in the K dimension
[int] --min_cc,--minimum-compute-capability Minimum device compute capability
[int] --max_cc,--maximum-compute-capability Maximum device compute capability
[enum] --raster_order={heuristic|H|along_m|M|along_n|N} If supported by kernel, sets the tile raster direction
[int] --swizzle_size={1,2,4,8} If supported by kernel, sets the 2D tile swizzle extent (In Hopper, other values will be rounded down to the nearest supported value)
Examples:
Profile a particular problem size:
$ cutlass_profiler --operation=Gemm --m=1024 --n=1024 --k=128
Schmoo over problem size and beta:
$ cutlass_profiler --operation=Gemm --m=1024:4096:256 --n=1024:4096:256 --k=128:8192:128 --beta=0,1,2.5
Schmoo over accumulator types:
$ cutlass_profiler --operation=Gemm --accumulator-type=f16,f32
Run when A is f16 with column-major and B is any datatype with row-major (For column major, use column, col, or n. For row major use, row or t):
$ cutlass_profiler --operation=Gemm --A=f16:column --B=*:row
Using various input value distribution:
$ cutlass_profiler --operation=Gemm --dist=uniform,min:0,max:3
$ cutlass_profiler --operation=Gemm --dist=gaussian,mean:0,stddev:3
$ cutlass_profiler --operation=Gemm --dist=sequential,start:0,delta:1
Using CUTLASS 3.x GEMM kernel with a tile scheduler that supports runtime tile remapping and raster mode order:
$ cutlass_profiler --operation=Gemm --m=2048 --n=2048 --k=2048 --raster_order=M --swizzle_size=2
Run a kernel with cta tile size of 256x128x32 and save workspace if results are incorrect (note that --cta-tile::k=32 is default cta-tile size):
$ cutlass_profiler --operation=Gemm --cta_m=256 --cta_n=128 --cta_k=32 --save-workspace=incorrect
Test your changes to gemm kernels with a quick functional test and save results in functional-test.csv:
$ cutlass_profiler --operation=Gemm \
--m=8,56,120,136,256,264,512,520,1024,1032,4096,8192,16384 \
--n=8,56,120,136,256,264,512,520,1024,1032,4096,8192,16384 \
--k=8,16,32,64,128,256,288,384,504,512,520 \
--beta=0,1,2 --profiling-iterations=1 \
--providers=cutlass --output=functional-test.csvThe format of tensor argument is followed by <type>:<layout>. The type could be f32 as 32-bit floating point, s8 as 8-bit signed integer, etc. The available types can be referred to the NumericTypeID_enumerants in util.cu. The layout could be row or column.
Example command line for profiling SGEMM kernels is as follows:
$ ./tools/profiler/cutlass_profiler --kernels=sgemm --m=3456 --n=4096 --k=4096
=============================
Problem ID: 1
Provider: CUTLASS
OperationKind: gemm
Operation: cutlass_simt_sgemm_128x128_8x2_nn_align1
Status: Success
Verification: ON
Disposition: Passed
cuBLAS: Passed
Arguments: --m=3456 --n=4096 --k=4096 --A=f32:column --B=f32:column --C=f32:column --alpha=1 --beta=0 --split_k_slices=1 \
--batch_count=1 --op_class=simt --accum=f32 --cta_m=128 --cta_n=128 --cta_k=8 --stages=2 --warps_m=4 \
--warps_n=2 --warps_k=1 --inst_m=1 --inst_n=1 --inst_k=1 --min_cc=50 --max_cc=1024
Bytes: 180355072 bytes
FLOPs: 115992428544 flops
Runtime: 6.73655 ms
Memory: 24.934 GiB/s
Math: 17218.4 GFLOP/sNote, the arguments which appear in the output may be used as command line parameters for subsequent invocations.
To execute kernels targeting Tensor Core operations, supply the flag --op_class=tensorop in the command line.
$ ./tools/profiler/cutlass_profiler --op_class=tensorop --m=3456 --n=4096 --k=8192
=============================
Problem ID: 1
Provider: CUTLASS
OperationKind: gemm
Operation: cutlass_tensorop_s16816gemm_f16_256x128_32x3_nn_align8
Status: Success
Verification: ON
Disposition: Passed
cuBLAS: Passed
Arguments: --m=3456 --n=4096 --k=8192 --A=f16:column --B=f16:column --C=f32:column --alpha=1 --beta=0 --split_k_slices=1 \
--batch_count=1 --op_class=tensorop --accum=f32 --cta_m=256 --cta_n=128 --cta_k=32 --stages=3 --warps_m=4 \
--warps_n=2 --warps_k=1 --inst_m=16 --inst_n=8 --inst_k=16 --min_cc=80 --max_cc=1024
Bytes: 180355072 bytes
FLOPs: 231956545536 flops
Runtime: 0.98647 ms
Memory: 170.272 GiB/s
Math: 235138 GFLOP/sAll arguments may have single values or comma-delimited set of values. Integers may also be specified
as an inclusive range with the following syntax start:end:increment or simply start:end.
For example, the following sweeps over the range of the GEMM K dimension from 8 to 4096 in increments of 8 elements.
$ ./tools/profiler/cutlass_profiler --kernels=cutlass_simt_sgemm_128x128_nn --m=4352 --n=4096 --k=8:4096:8By default, runtime and computed GFLOP/s are reported for each operation and problem size. Additionally,
a table of comma separated values are reported at the end of the execution. This may be output to a file
with the --output=<filename.csv> command line option as shown:
$ ./tools/profiler/cutlass_profiler --kernels=cutlass_simt_sgemm_128x128_nn \
--m=3456 --n=4096 --k=8:4096:8 --output=report.csvTo faclitate generation of pivot tables and charts, additional columns may be prepended with the
--tags=<column>:<value> option. One or more tags may be specified using a comma-delimited list.
$ ./tools/profiler/cutlass_profiler --kernels=cutlass_simt_sgemm_128x128_nn \
--m=3456 --n=4096 --k=8:4096:8 --output=report.csv \
--tags=cutlass:2.2,date:2020-06-08CUTLASS 3.0 introduces a new naming convention for GEMMs used by the profiler targeting the NVIDIA Hopper architecture and beyond so as to indicate new features of the kernel within the name (e.g., the cluster shape).
To best illustrate this naming convention, we will walk through the meaning of each of the components in a GEMM kernel used by the profiler:
cutlass3x_sm90_tensorop_s64x128x16gemm_f16_f16_f32_f16_f32_128x128x64_2x1x1_0_ntn_align8
The components within this name are as follows:
cutlass3x: indicates that the kernel was generated through the CUTLASS 3.0 APIsm90: indicates that the kernel targets NVIDIA GPUs with compute capability 90tensorop: indicates that the kernel makes use of NVIDIA Tensor Cores (as opposed tosimt, which indicates the use of "CUDA cores")s: indicates that the Tensor Core instruction being used accumulates in single precision (as opposed toh, which indicates half precision)64x128x16gemm: indicates that the shape of the Tensor Core instruction being used (MxNxK) is 64x128x16f16_f16_f32_f16_f16: indicates that the data types for operands A, B, Accumulator, C and D (in that order).128x128x64: indicates that the thread block shape used in the GEMM (MxNxK) is 128x128x642x1x1: indicates that the cluster shape being used is 2x1x10: indicates that the kernel uses the CollectiveBuilder's automatic stage calculation to determine the number of pipeline stages in the kernel. Note that0does not mean that no stages are used. A nonzero value indicates that automatic stage calculation is not performed and indicates the number of pipeline stages to be used. This 0 is only added to the kernel's procedural name, the profiler will still report the actual stage count when printing the kernel argument details (--stages=N) and kernel discovery will still support filtering through the--stagesargument.ntn: indicates that the layouts for operands A, B, and C are column major ("n"; non-transposed), row major ("t"; transposed), and column major, respectively.align8: indicates that the maximum alignment between operands A and B is 8.
Note that in some special cases where the input A/B types do not match that of the MMA instruction's, the MMA facing input type is added to the instruction string as well.
cutlass3x_sm90_tensorop_s64x128x8tf32gemm_f32_f32_f32_f32_f32_128x128x32_2x1x1_0_tnn_align4
s64x128x8tf32gemm: indicates that the MMA consumes inputs intf32format, and therefore the kernel performs rounding of thef32values in global memory while loading them into shared memory.
For custom mainloop or epilogue schedules, details of the opted-in schedule are appended to the end of the kernel name. For example,
cutlass3x_sm90_tensorop_h64x128x16gemm_f16_f16_f16_void_f16_128x128x64_1x1x1_0_nnn_align8_warpspecialized_cooperative_epi_tma
warpspecialized_cooperative: Mainloop employs a persistent warp-specialized mainloop and kernel schedule.epi_tma: Kernel epilogue employs TMA based vectorization.f16_f16_f16_void_f16: In this case, C type is set tovoid, indicating that residual matrix support is disabled.
The CUTLASS Profiler is capable of executing 2-D and 3-D convolution problems for forwards and backwards operator variants.
The CUTLASS Profiler can be built with cuDNN enabled to use as a reference implementation. If CMake detects
the cuDNN library available in the system, it is included as a dependency. This may be explicitly overridden
with CMake flag CUTLASS_ENABLE_CUDNN.
$ cmake .. -DCUTLASS_LIBRARY_OPERATIONS=conv2d -DCUTLASS_ENABLE_CUDNN=OFF
...
$ make -j16 cutlass_profiler$ ./tools/profiler/cutlass_profiler --help --operation=Conv2d
Conv2d
[enum] --conv_kind Convolutional operator (fprop, dgrad, wgrad)
[int] --n,--input_n Input N dimension of the Conv2d problem space
[int] --h,--input_h Input H dimension of the Conv2d problem space
[int] --w,--input_w Input W dimension of the Conv2d problem space
[int] --c,--input_c Input C dimension of the Conv2d problem space
[int] --k,--filter_k Filter K dimension of the Conv2d problem space
[int] --r,--filter_r Filter R dimension of the Conv2d problem space
[int] --s,--filter_s Filter S dimension of the Conv2d problem space
[int] --p,--output_p Output P dimension of the Conv2d problem space
[int] --q,--output_q Output Q dimension of the Conv2d problem space
[int] --g,--groups Number of convolution groups
[int] --pad_h Padding in H direction
[int] --pad_w Padding in W direction
[int] --stride_h Stride in H direction
[int] --stride_w Stride in W direction
[int] --dilation_h Dilation in H direction
[int] --dilation_w Dilation in W direction
[tensor] --Activation Tensor storing the Activation operand
[tensor] --Filter Tensor storing the Filter operand
[tensor] --Output Tensor storing the Output operand
[enum] --conv_mode Convolution filter mode (conv, cross)
[enum] --iterator_algorithm,--iterator_algo Convolution iterator algorithm (analytic, optimized)
[scalar] --alpha,--epilogue::alpha Epilogue scalar alpha
[scalar] --beta,--epilogue::beta Epilogue scalar beta
[enum] --split_k_mode,--split-k-mode SplitK mode for serial or parallel reduction (serial, parallel)
[int] --split_k_slices,--split-k-slices Number of partitions of K dimension
[enum] --eq_gemm_provider,--eq-gemm-provider Enable profiling equivalent gemm by the following providers (cutlass)
[enum] --op_class,--opcode-class Class of math instruction (simt, tensorop, wmmatensorop, wmma)
[enum] --accum,--accumulator-type Math instruction accumulator data type
[int] --cta_m,--threadblock-shape::m Threadblock shape in the M dimension
[int] --cta_n,--threadblock-shape::n Threadblock shape in the N dimension
[int] --cta_k,--threadblock-shape::k Threadblock shape in the K dimension
[int] --cluster_m,--cluster-shape::m Cluster shape in the M dimension
[int] --cluster_n,--cluster-shape::n Cluster shape in the N dimension
[int] --cluster_k,--cluster-shape::k Cluster shape in the K dimension
[int] --stages,--threadblock-stages Number of stages of threadblock-scoped matrix multiply
[int] --warps_m,--warp-count::m Number of warps within threadblock along the M dimension
[int] --warps_n,--warp-count::n Number of warps within threadblock along the N dimension
[int] --warps_k,--warp-count::k Number of warps within threadblock along the K dimension
[int] --inst_m,--instruction-shape::m Math instruction shape in the M dimension
[int] --inst_n,--instruction-shape::n Math instruction shape in the N dimension
[int] --inst_k,--instruction-shape::k Math instruction shape in the K dimension
[int] --min_cc,--minimum-compute-capability Minimum device compute capability
[int] --max_cc,--maximum-compute-capability Maximum device compute capability
Examples:
Profile a particular convolution (specify all the convolution parameters):
$ cutlass_profiler --operation=Conv2d --Activation=f16:nhwc --Filter=f16:nhwc --Output=f16 --accumulator-type=f32 --n=32 --h=14 --w=14 --c=8 --k=64 --r=3 --s=3 --pad_h=1 --pad_w=1 --stride_h=1 --stride_w=1 --dilation_h=1 --dilation_w=1
Example command line for profiling forward propagation convolution kernels on CUDA cores is as follows:
$ ./tools/profiler/cutlass_profiler --kernels=simt_sfprop --verification-providers=device --n=8 --h=224 --w=224 --c=128 --k=128 --r=3 --s=3
=============================
Problem ID: 1
Provider: CUTLASS
OperationKind: conv2d
Operation: cutlass_simt_sfprop_optimized_128x128_8x2_nhwc
Status: Success
Verification: ON
Disposition: Passed
reference_device: Passed
Arguments: --conv_kind=fprop --n=8 --h=224 --w=224 --c=128 --k=128 --r=3 --s=3 --p=224 --q=224 --pad_h=1 --pad_w=1 \
--stride_h=1 --stride_w=1 --dilation_h=1 --dilation_w=1 --Activation=f32:nhwc --Filter=f32:nhwc --Output=f32:nhwc \
--conv_mode=cross --iterator_algorithm=optimized --alpha=1 --beta=0 --split_k_mode=serial --split_k_slices=1 \
--eq_gemm_provider=none --op_class=simt --accum=f32 --cta_m=128 --cta_n=128 --cta_k=8 --stages=2 --warps_m=4 \
--warps_n=2 --warps_k=1 --inst_m=1 --inst_n=1 --inst_k=1 --min_cc=50 --max_cc=1024
Bytes: 2055798784 bytes
FLOPs: 118482796544 flops
Runtime: 8.13237 ms
Memory: 235.431 GiB/s
Math: 14569.3 GFLOP/s
Example command line for profiling forward propagation convolution kernels runing on Tensor Cores is as follows:
$ ./tools/profiler/cutlass_profiler --kernels=tensorop*fprop --verification-providers=device --n=8 --h=224 --w=224 --c=128 --k=128 --r=3 --s=3
=============================
Problem ID: 1
Provider: CUTLASS
OperationKind: conv2d
Operation: cutlass_tensorop_s16816fprop_optimized_f16_128x128_64x4_nhwc
Status: Success
Verification: ON
Disposition: Passed
reference_device: Passed
Arguments: --conv_kind=fprop --n=8 --h=224 --w=224 --c=128 --k=128 --r=3 --s=3 --p=224 --q=224 --pad_h=1 --pad_w=1 \
--stride_h=1 --stride_w=1 --dilation_h=1 --dilation_w=1 --Activation=f16:nhwc --Filter=f16:nhwc --Output=f32:nhwc \
--conv_mode=cross --iterator_algorithm=optimized --alpha=1 --beta=0 --split_k_mode=serial --split_k_slices=1 \
--eq_gemm_provider=none --op_class=tensorop --accum=f32 --cta_m=128 --cta_n=128 --cta_k=64 --stages=4 \
--warps_m=2 --warps_n=2 --warps_k=1 --inst_m=16 --inst_n=8 --inst_k=16 --min_cc=80 --max_cc=1024
Bytes: 1130659840 bytes
FLOPs: 118482796544 flops
Runtime: 0.945071 ms
Memory: 1114.21 GiB/s
Math: 125369 GFLOP/s
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