explict type in for, use func not macro to skip, remove dup test

Co-authored-by: Tobias Ribizel <ribizel@kit.edu>

common/factorization/par_ilut_select_kernels.hpp.inc

@@ -62,8 +62,7 @@ __global__ __launch_bounds__(searchtree_width) void build_searchtree(
     // assuming rounding towards zero
     auto stride = double(size) / sample_size;
 #pragma unroll
-    for (auto i = decltype(sampleselect_oversampling){0};
-         i < sampleselect_oversampling; ++i) {
+    for (int i = 0; i < sampleselect_oversampling; ++i) {
         auto lidx = idx * sampleselect_oversampling + i;
         auto val = input[static_cast<IndexType>(lidx * stride)];
         samples[i] = abs(val);
@@ -120,8 +119,7 @@ __global__ __launch_bounds__(default_block_size) void count_buckets(
         auto el = abs(input[i]);
         IndexType tree_idx{};
 #pragma unroll
-        for (auto level = decltype(sampleselect_searchtree_height){0};
-             level < sampleselect_searchtree_height; ++level) {
+        for (int level = 0; level < sampleselect_searchtree_height; ++level) {
             auto cmp = !(el < sh_tree[tree_idx]);
             tree_idx = 2 * tree_idx + 1 + cmp;
         }
@@ -170,7 +168,7 @@ __global__ __launch_bounds__(default_block_size) void block_prefix_sum(
     // compute prefix sum over warp-sized blocks
     IndexType total{};
     auto base_idx = warp_idx * work_per_warp * warp.size();
-    for (auto step = decltype(work_per_warp){0}; step < work_per_warp; ++step) {
+    for (IndexType step = 0; step < work_per_warp; ++step) {
         auto idx = warp_lane + step * warp.size() + base_idx;
         auto val = idx < num_blocks ? local_counters[idx] : zero<IndexType>();
         IndexType warp_total{};

cuda/test/components/sorting_kernels.cu

@@ -99,7 +99,7 @@ protected:
     {
         // we want some duplicate elements
         std::uniform_int_distribution<gko::int32> dist(0, num_elements / 2);
-        for (auto i = decltype(num_elements){0}; i < num_elements; ++i) {
+        for (int i = 0; i < num_elements; ++i) {
             ref_shared.get_data()[i] = dist(rng);
         }
         ddata = gko::Array<gko::int32>{cuda, ref_shared};

dpcpp/CMakeLists.txt

@@ -75,7 +75,7 @@ else ()
     target_link_options(ginkgo_dpcpp PUBLIC -fsycl-device-code-split=per_kernel)
 endif()
 target_link_libraries(ginkgo_dpcpp PUBLIC ginkgo_device)
-target_link_libraries(ginkgo_dpcpp PRIVATE $<LINK_ONLY:MKL::MKL_DPCPP>)
+target_link_libraries(ginkgo_dpcpp PRIVATE MKL::MKL_DPCPP)
 if (GINKGO_DPCPP_SINGLE_MODE)
     target_compile_definitions(ginkgo_dpcpp PRIVATE GINKGO_DPCPP_SINGLE_MODE=1)
 endif()