Added numa_support rfc

rfcs/proposed/simplified_numa_support/README.md

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+# Simplified NUMA support in oneTBB
+
+## Introduction
+
+In Non-Uniform Memory Access (NUMA) systems, the cost of memory accesses depends on the
+*nearness* of the processor to the memory resource on which the accessed data resides. 
+While oneTBB has core support that enables developers to tune for Non-Uniform Memory 
+Access (NUMA) systems, we believe this support can be simplified and improved to provide 
+an improved user experience.  
+
+This early proposal recommends addressing for areas for improvement:
+
+1. improved reliability of HWLOC-dependent topology and pinning support in,
+2. addition of a NUMA-aware allocation,
+3. simplified approaches to associate task distribution with data placement and 
+4. where possible, improved out-of-the-box performance for high-level oneTBB features.
+
+We expect that this draft proposal may be broken into smaller proposals based on feedback 
+and prioritization of the suggested features.
+
+The features for NUMA tuning already available in the oneTBB 1.3 specification include:
+
+- Functions in the `tbb::info` namespace **[info_namespace]** 
+  - `std::vector<numa_node_id> numa_nodes()`
+  - `int default_concurrency(numa_node_id id = oneapi::tbb::task_arena::automatic)`
+- `tbb::task_arena::constraints` in **[scheduler.task_arena]**
+
+Below is the example that demonstrates the use of these APIs to pin threads to different 
+arenas to each of the NUMA nodes available on a system, submit work across those `task_arena` 
+objects and into associated `task_group`` objects, and then wait for work again using both 
+the `task_arena` and `task_group` objects.
+
+    #include "oneapi/tbb/task_group.h"
+    #include "oneapi/tbb/task_arena.h"
+
+    #include <vector>
+
+    int main() {
+        std::vector<oneapi::tbb::numa_node_id> numa_nodes = oneapi::tbb::info::numa_nodes();
+        std::vector<oneapi::tbb::task_arena> arenas(numa_nodes.size());
+        std::vector<oneapi::tbb::task_group> task_groups(numa_nodes.size());
+
+        // Initialize the arenas and place memory
+        for (int i = 0; i < numa_nodes.size(); i++) {
+            arenas[i].initialize(oneapi::tbb::task_arena::constraints(numa_nodes[i]));
+            arenas[i].execute([i] {
+              // allocate/place memory on NUMA node i
+            });
+        }
+        
+        for (int j 0; j < NUM_STEPS; ++i) {
+
+          // Distribute work across the arenas / NUMA nodes
+          for (int i = 0; i < numa_nodes.size(); i++) {
+            arenas[i].execute([&task_groups, i] {
+              task_groups[i].run([] {
+                /* executed by the thread pinned to specified NUMA node */
+              });
+            });
+          }
+
+          // Wait for the work in each arena / NUMA node to complete
+          for (int i = 0; i < numa_nodes.size(); i++) {
+            arenas[i].execute([&task_groups, i] {
+                task_groups[i].wait();
+            });
+          }
+        }
+
+        return 0;
+    }
+
+### The need for application-specific knowledge
+
+In general when tuning a parallel application for NUMA systems, the goal is to expose sufficient
+parallelism while minimizing (or at least controlling) data access and communication costs. The 
+tradeoffs involved in this tuning often rely on application-specific knowledge. 
+
+In particular, NUMA tuning typically involves:
+
+1. Understanding the overall application problem and its use of algorithms and data containers
+2. Placement of data container objects onto memory resources
+3. Distribution of tasks to hardware resources that optimize for data placement
+
+As shown in the previous example, the oneTBB 1.3 specification only provides low-level
+support for NUMA optimization. The `tbb::info` namespace provides topology discovery. And the
+combination of `task_arena`, `task_arena::constraints` and `task_group` provide a mechanism for
+placing tasks onto specific processors. There is no high-level support for memory allocation
+or placement, or for guiding the task distribution of algorithms.
+
+### Issues that should be resolved in the oneTBB library
+
+**The behavior of existing features is not always predictable.** There is a note in 
+section **[info_namespace]** of the oneTBB specification that describes
+the function `std::vector<numa_node_id> numa_nodes()`, "If error occurs during system topology 
+parsing, returns vector containing single element that equals to `task_arena::automatic`."  
+
+In practice, the error often occurs because HWLOC is not detected on the system. While the 
+oneTBB documentation states in several places that HWLOC is required for NUMA support and 
+even provides guidance on 
+[how to check for HWLOC](https://www.intel.com/content/www/us/en/docs/onetbb/get-started-guide/2021-12/next-steps.html), 
+the failure to resolve HWLOC at runtime silently returns a default of `task_arena::automatic`. This
+default does not pin threads to NUMA nodes. It is too easy to write code similar to the preceding 
+example and be unaware that a HWLOC installation error (or lack of HWLOC) has undone all your effort.
+
+**Getting good performance using these tools requres notable manual coding effort by users.** As we 
+can see in the preceding example, if we want to spread work across the NUMA nodes in 
+a system we need to query the topology using functions in the `tbb::info` namespace, create
+one `task_arena` per NUMA node, along with one `task_group` per NUMA node, and then add an
+extra loop that iterates overs these `task_arena` and `task_group` objects to execute the
+work on the desired NUMA nodes. We also need to handle all container allocations using OS-specific
+APIs (or behaviors, such as first-touch) to allocator or place them on the appropriate NUMA nodes.
+
+**The out-of-the-box performance of the generic TBB APIs on NUMA systems is not good enough.**
+Should the oneTBB library do anything special be default if the system is a NUMA system?  Or should 
+regular random stealing distribute the work across all of the cores, regardless of which NUMA first 
+touched the data?
+
+Is it reasonable for a developer to expect that a series of loops, such as the ones that follow, will
+try to create a NUMA-friendly distribution of tasks so that accesses to the same elements of `b` and `c`
+in the two loops are from the same NUMA nodes? Or is this too much to expect without providing hints? 
+
+    tbb::parallel_for(0, N, 
+      [](int i) { 
+        b[i] = f(i);
+        c[i] = g(i); 
+      });
+
+    tbb::parallel_for(0, N, 
+      [](int i) { 
+        a[i] = b[i] + c[i]; 
+      });
+
+## Proposal
+
+### Increased availability of NUMA support
+
+The oneTBB 1.3 specification states for `tbb::info::numa_nodes`, "If error occurs during system 
+topology parsing, returns vector containing single element that equals to task_arena::automatic."
+
+Since the oneTBB library dynamically loads the HWLOC library, a misconfiguration can cause the HWLOC
+to fail to be found. In that case, a call like:
+
+    std::vector<oneapi::tbb::numa_node_id> numa_nodes = oneapi::tbb::info::numa_nodes();
+
+will return a vector with a single element of `task_arena::automatic`. This behavior, as we have noticed
+through user questions, can lead to unexpected performance from NUMA optimizations. When running
+on a NUMA system, a developer that has not fully read the documentation may expect that `numa_nodes()`
+will give a proper accounting of the NUMA nodes. When the code, without raising any alarm, returns only 
+a single, valid element due to the environmental configuation (such as lack of HWLOCK), it is too easy 
+for developers to not notice that the code is acting in a valid, but unexpected way.
+
+We propose that the oneTBB library implementation include, wherever possibly, a statically-linked fallback 
+to decrease that likelihood of such failures. The oneTBB specification will remain unchanged.
+
+### NUMA-aware allocation
+
+We will define allocators of other features that simplify the process of allocating or places data onto
+specific NUMA nodes.
+
+### Simplified approaches to associate task distribution with data placement
+
+As discussed earlier, NUMA-aware allocation is just the first step in optimizing for NUMA architectures.
+We also need to deliver mechanisms to guide task distribution so that tasks are executed on execution
+resources that are near to the data they access. oneTBB already provides low-level support through
+`tbb::info` and `tbb::task_arena`, but we should up-level this support into the high-level algorithms,
+flow graph and containers where appropriate.
+
+### Improved out-of-the-box performance for high-level oneTBB features.
+
+For high-level oneTBB features that are modified to provide improved NUMA support, we should try to 
+align default behaviors for those features with user-expectations when used on NUMA systems.
+
+## Open Questions
+
+1. Do we need simplified support, or are users that want NUMA support in oneTBB
+willing to, or perhaps even prefer, to manage the details manually?
+2. Is it reasonable to expect good out-of-the-box performance on NUMA systems 
+without user hints or guidance.