Remove CUDA 10 compatibility code. (#12088)

This PR updates some documentation and removes some compatibility layers referencing CUDA 10, which is no longer supported by the package.

Authors:
  - Bradley Dice (https://github.com/bdice)

Approvers:
  - GALI PREM SAGAR (https://github.com/galipremsagar)
  - David Wendt (https://github.com/davidwendt)
  - Lawrence Mitchell (https://github.com/wence-)

URL: #12088

cpp/src/copying/concatenate.cu

@@ -180,10 +180,8 @@ __global__ void fused_concatenate_kernel(column_device_view const* input_views,
   if (Nullable) { active_mask = __ballot_sync(0xFFFF'FFFFu, output_index < output_size); }
   while (output_index < output_size) {
     // Lookup input index by searching for output index in offsets
-    // thrust::prev isn't in CUDA 10.0, so subtracting 1 here instead
-    auto const offset_it =
-      -1 + thrust::upper_bound(
-             thrust::seq, input_offsets, input_offsets + num_input_views, output_index);
+    auto const offset_it            = thrust::prev(thrust::upper_bound(
+      thrust::seq, input_offsets, input_offsets + num_input_views, output_index));
     size_type const partition_index = offset_it - input_offsets;
 
     // Copy input data to output

cpp/src/rolling/detail/rolling.cuh

@@ -122,10 +122,8 @@ struct DeviceRolling {
     using AggOp = typename corresponding_operator<op>::type;
     AggOp agg_op;
 
-    // declare this as volatile to avoid some compiler optimizations that lead to incorrect results
-    // for CUDA 10.0 and below (fixed in CUDA 10.1)
-    volatile cudf::size_type count = 0;
-    OutputType val                 = AggOp::template identity<OutputType>();
+    cudf::size_type count = 0;
+    OutputType val        = AggOp::template identity<OutputType>();
 
     for (size_type j = start_index; j < end_index; j++) {
       if (!has_nulls || input.is_valid(j)) {
@@ -190,11 +188,9 @@ struct DeviceRollingArgMinMaxString : DeviceRollingArgMinMaxBase<cudf::string_vi
     using AggOp     = typename corresponding_operator<op>::type;
     AggOp agg_op;
 
-    // declare this as volatile to avoid some compiler optimizations that lead to incorrect results
-    // for CUDA 10.0 and below (fixed in CUDA 10.1)
-    volatile cudf::size_type count = 0;
-    InputType val                  = AggOp::template identity<InputType>();
-    OutputType val_index           = default_output;
+    cudf::size_type count = 0;
+    InputType val         = AggOp::template identity<InputType>();
+    OutputType val_index  = default_output;
 
     for (size_type j = start_index; j < end_index; j++) {
       if (!has_nulls || input.is_valid(j)) {
@@ -284,13 +280,11 @@ struct DeviceRollingCountValid {
                              size_type end_index,
                              size_type current_index)
   {
-    // declare this as volatile to avoid some compiler optimizations that lead to incorrect
-    // results for CUDA 10.0 and below (fixed in CUDA 10.1)
-    volatile cudf::size_type count = 0;
-
     bool output_is_valid = ((end_index - start_index) >= min_periods);
 
     if (output_is_valid) {
+      cudf::size_type count = 0;
+
       if (!has_nulls) {
         count = end_index - start_index;
       } else {

cpp/src/rolling/jit/kernel.cu

@@ -58,10 +58,6 @@ __global__ void gpu_rolling_new(cudf::size_type nrows,
 
   auto active_threads = __ballot_sync(0xffff'ffffu, i < nrows);
   while (i < nrows) {
-    // declare this as volatile to avoid some compiler optimizations that lead to incorrect results
-    // for CUDA 10.0 and below (fixed in CUDA 10.1)
-    volatile cudf::size_type count = 0;
-
     int64_t const preceding_window = get_window(preceding_window_begin, i);
     int64_t const following_window = get_window(following_window_begin, i);
 
@@ -77,8 +73,8 @@ __global__ void gpu_rolling_new(cudf::size_type nrows,
     // TODO: We should explore using shared memory to avoid redundant loads.
     //       This might require separating the kernel into a special version
     //       for dynamic and static sizes.
-    count       = end_index - start_index;
-    OutType val = agg_op::template operate<OutType, InType>(in_col, start_index, count);
+    cudf::size_type count = end_index - start_index;
+    OutType val           = agg_op::template operate<OutType, InType>(in_col, start_index, count);
 
     // check if we have enough input samples
     bool const output_is_valid = (count >= min_periods);

cpp/src/strings/copying/concatenate.cu

@@ -127,10 +127,8 @@ __global__ void fused_concatenate_string_offset_kernel(column_device_view const*
   if (Nullable) { active_mask = __ballot_sync(0xFFFF'FFFFu, output_index < output_size); }
   while (output_index < output_size) {
     // Lookup input index by searching for output index in offsets
-    // thrust::prev isn't in CUDA 10.0, so subtracting 1 here instead
-    auto const offset_it =
-      -1 + thrust::upper_bound(
-             thrust::seq, input_offsets, input_offsets + num_input_views, output_index);
+    auto const offset_it            = thrust::prev(thrust::upper_bound(
+      thrust::seq, input_offsets, input_offsets + num_input_views, output_index));
     size_type const partition_index = offset_it - input_offsets;
 
     auto const offset_index      = output_index - *offset_it;
@@ -180,10 +178,8 @@ __global__ void fused_concatenate_string_chars_kernel(column_device_view const*
 
   while (output_index < output_size) {
     // Lookup input index by searching for output index in offsets
-    // thrust::prev isn't in CUDA 10.0, so subtracting 1 here instead
-    auto const offset_it =
-      -1 + thrust::upper_bound(
-             thrust::seq, partition_offsets, partition_offsets + num_input_views, output_index);
+    auto const offset_it            = thrust::prev(thrust::upper_bound(
+      thrust::seq, partition_offsets, partition_offsets + num_input_views, output_index));
     size_type const partition_index = offset_it - partition_offsets;
 
     auto const offset_index = output_index - *offset_it;

python/custreamz/README.md

@@ -48,8 +48,8 @@ Please see the [Demo Docker Repository](https://hub.docker.com/r/rapidsai/rapids
 
 ### CUDA/GPU requirements
 
-* CUDA 10.0+
-* NVIDIA driver 410.48+
+* CUDA 11.0+
+* NVIDIA driver 450.80.02+
 * Pascal architecture or better (Compute Capability >=6.0)
 
 ### Conda