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CUB Testing Overview

Warning

CUB is in the progress of migrating to [Catch2](https://github.com/catchorg/Catch2) framework.

CUB tests rely on CPM to fetch Catch2 that's used as our main testing framework along with metal that's used as template metaprogramming backend for some of the test macro implementation.

Currently, legacy tests coexist with Catch2 ones. This guide is focused on new tests.

Important

Instead of including <catch2/catch.hpp> directly, use catch2_test_helper.h. Also, the helper header has to be included after all CUB headers, otherwise the test won't catch some of the warnings in CUB kernels:

#include <cub/block/block_scan.cuh>
#include <thrust/host_vector.h>
#include "catch2_test_helper.h"

Directory and File Naming

Our tests can be found in the test directory. Legacy tests have the following naming scheme: test_SCOPE_FACILITY.cu. For instance, here are the reduce tests:

test/test_warp_reduce.cu
test/test_block_reduce.cu
test/test_device_reduce.cu

Catch2-based tests have a different naming scheme: catch2_test_SCOPE_FACILITY.cu.

The prefix is essential since that's how CMake finds tests and distinguishes new tests from legacy ones.

Test Structure

Base case

Let's start with a simple example. Say there's no need to cover many types with your test.

// 0) Define test name and tags
CUB_TEST("SCOPE FACILITY works with CONDITION",
        "[FACILITY][SCOPE]")
{
  using type = std::int32_t;
  constexpr int threads_per_block = 256;
  constexpr int num_items = threads_per_block;

  // 1) Allocate device input
  thrust::device_vector<type> d_input(num_items);

  // 2) Generate 3 random input arrays using Catch2 helper
  c2h::gen(CUB_SEED(3), d_input);

  // 3) Allocate output array
  thrust::device_vector<type> d_output(d_input.size());

  // 4) Copy device input to host
  thrust::host_vector<key_t> h_reference = d_input;

  // 5) Compute reference output
  std::ALGORITHM(
      thrust::raw_pointer_cast(h_reference.data()),
      thrust::raw_pointer_cast(h_reference.data()) + h_reference.size());

  // 6) Compute CUB output
  SCOPE_ALGORITHM<threads_per_block>(d_input.data(),
                                    d_output.data(),
                                    d_input.size());

  // 7) Compare device and host results
  REQUIRE( d_input == d_output );
}

We introduce test cases with the CUB_TEST macro in (0). This macro always takes two string arguments - a free-form test name and one or more tags. Then, in (1), we allocate device memory using thrust::device_vector. The memory is filled with random data in (2).

Generator c2h::gen takes two parameters. The first one is a random generator seed. Instead of providing a single value, we use the CUB_SEED macro. The macro expects a number of seeds that has to be generated. In the example above, we require three random seeds to be generated. This leads to the whole test being executed three times with different seed values.

Later, in (3) and (4), we allocate device output and host reference. Then, in (4), we populate host input data and perform reference computation on the host in (5). Then launch the CUB algorithm in (6). At this point, we have a reference solution on CPU and CUB solution on GPU. The two can be compared with REQUIRE assert.

Warning

Standard algorithms (std::) have to be used as much as possible when computing reference solutions.

If your test has to cover floating point types, it's sufficient to replace REQUIRE( a == b ) with REQUIRE_APPROX_EQ(a, b).

It's strongly advised to always use c2h::gen to produce input data. Other data generation methods might be used if absolutely necessary in tests of corner cases.

If a custom type has to be tested, the following helper should be used:

using type = c2h::custom_type_t<c2h::accumulateable_t,
                                c2h::equal_comparable_t>;

Here we enumerate all the type properties that we are interested in. The produced type ends up having operator== and operator+. There are more properties implemented. If some property is missing, it'd be better to add one in c2h instead of writing a custom type from scratch.

Type Lists

Since CUB is a generic library, it's often required to test CUB algorithms against many types. To do so, it's sufficient to define a type list and provide it to the CUB_TEST macro.

// 0) Define type list
using types = c2h::type_list<std::uint8_t, std::int32_t>;

CUB_TEST("SCOPE FACILITY works with CONDITION",
        "[FACILITY][SCOPE]",
        types) // 1) Provide it to the test case
{
  // 2) Access current type with `c2h::get`
  using type = typename c2h::get<0, TestType>;
  // ...
}

This will lead to the test running two times. The first run will cause the type to be std::uint8_t. The second one will cause type to be std::uint32_t.

Warning

It's important to use types in std:: instead of primitive types like char and int.

Multidimensional Configuration Spaces

In most cases, the input data type is not the only compile-time parameter we want to vary. For instance, you might need to test a block algorithm for different data types and different thread block sizes. To do so, you can add another type list as follows:

using block_sizes = c2h::enum_type_list<int, 128, 256>;
using types = c2h::type_list<std::uint8_t, std::int32_t>;

CUB_TEST("SCOPE FACILITY works with CONDITION",
        "[FACILITY][SCOPE]",
        types,
        block_sizes)
{
  using type = typename c2h::get<0, TestType>;
  constexpr int threads_per_block = c2h::get<1, TestType>::value;
  // ...
}

The code above leads to the following combinations being compiled:

  • type = std::uint8_t, threads_per_block = 128
  • type = std::uint8_t, threads_per_block = 256
  • type = std::int32_t, threads_per_block = 128
  • type = std::int32_t, threads_per_block = 256

Speedup Compilation Time

Since type lists in the CUB_TEST form a Cartesian product, compilation time grows quickly with every new dimension. To keep the compilation process parallelized, it's possible to rely on %PARAM% machinery:

// %PARAM% BLOCK_SIZE bs 128:256
using block_sizes = c2h::enum_type_list<int, BLOCK_SIZE>;
using types = c2h::type_list<std::uint8_t, std::int32_t>;

CUB_TEST("SCOPE FACILITY works with CONDITION",
        "[FACILITY][SCOPE]",
        types,
        block_sizes)
{
  using type = typename c2h::get<0, TestType>;
  constexpr int threads_per_block = c2h::get<1, TestType>::value;
  // ...
}

The comment with %PARAM% is recognized at CMake level. It leads to multiple executables being produced from a single test source.

bin/cub.test.scope_algorithm.bs_128
bin/cub.test.scope_algorithm.bs_256

Multiple %PARAM% comments can be specified forming another Cartesian product.

Final Test

Let's consider the final test that illustrates all of the tools we discussed above:

// %PARAM% BLOCK_SIZE bs 128:256
using block_sizes = c2h::enum_type_list<int, BLOCK_SIZE>;
using types = c2h::type_list<std::uint8_t, std::int32_t>;

CUB_TEST("SCOPE FACILITY works with CONDITION",
        "[FACILITY][SCOPE]",
        types,
        block_sizes)
{
  using type = typename c2h::get<0, TestType>;
  constexpr int threads_per_block = c2h::get<1, TestType>::value;
  constexpr int max_num_items = threads_per_block;

  thrust::device_vector<type> d_input(
    GENERATE_COPY(take(2, random(0, max_num_items))));
  c2h::gen(CUB_SEED(3), d_input);

  thrust::device_vector<type> d_output(d_input.size());

  SCOPE_ALGORITHM<threads_per_block>(d_input.data(),
                                    d_output.data(),
                                    d_input.size());

  REQUIRE( d_input == d_output );
}

Apart from discussed tools, here we also rely on Catch2 to generate random input sizes in [0, max_num_items] range. Overall, the test will produce two executables. Each of these executables is going to generate 2 input problem sizes. For each problem size, 3 random vectors are generated. As a result, we have 12 different tests.