![]() |
Home | Libraries | People | FAQ | More |
Modern micro-processors contain integrated memory controllers that are connected
via channels to the memory. Accessing the memory can be organized in two kinds:
Uniform Memory Access (UMA) and Non-Uniform Memory Access (NUMA).
In contrast to UMA, that provides a centralized pool of memory (and thus does
not scale after a certain number of processors), a NUMA architecture divides
the memory into local and remote memory relative to the micro-processor.
Local memory is directly attached to the processor's integrated memory controller.
Memory connected to the memory controller of another micro-processor (multi-socket
systems) is considered as remote memory. If a memory controller access remote
memory it has to traverse the interconnect[8] and connect to the remote memory controller.
Thus accessing
remote memory adds additional latency overhead to local memory access. Because
of the different memory locations, a NUMA-system experiences non-uniform
memory access time.
As a consequence the best performance is achieved
by keeping the memory access local.
Because only a subset of the NUMA-functionality is exposed by several operating systems, Boost.Fiber provides only a minimalistic NUMA API.
![]() |
Important |
---|---|
In order to enable NUMA support, b2 property |
![]() |
Important |
---|---|
MinGW using pthread implementation is not supported on Windows. |
Table 1.1. Supported functionality/operating systems
AIX |
FreeBSD |
HP/UX |
Linux |
Solaris |
Windows |
|
---|---|---|---|---|---|---|
pin thread |
+ |
+ |
+ |
+ |
+ |
+ |
logical CPUs/NUMA nodes |
+ |
+ |
+ |
+ |
+ |
+[a] |
NUMA node distance |
- |
- |
- |
+ |
- |
- |
tested on |
AIX 7.2 |
FreeBSD 11 |
- |
Arch Linux (4.10.13) |
OpenIndiana HIPSTER |
Windows 10 |
[a] Windows organizes logical cpus in groups of 64; boost.fiber maps {group-id,cpud-id} to a scalar equivalent to cpu ID of Linux (64 * group ID + cpu ID). |
In order to keep the memory access local as possible, the NUMA topology must be evaluated.
std::vector< boost::fibers::numa::node > topo = boost::fibers::numa::topology(); for ( auto n : topo) { std::cout << "node: " << n.id << " | "; std::cout << "cpus: "; for ( auto cpu_id : n.logical_cpus) { std::cout << cpu_id << " "; } std::cout << "| distance: "; for ( auto d : n.distance) { std::cout << d << " "; } std::cout << std::endl; } std::cout << "done" << std::endl; output: node: 0 | cpus: 0 1 2 3 4 5 6 7 16 17 18 19 20 21 22 23 | distance: 10 21 node: 1 | cpus: 8 9 10 11 12 13 14 15 24 25 26 27 28 29 30 31 | distance: 21 10 done
The example shows that the systems consits out of 2 NUMA-nodes, to each NUMA-node
belong 16 logical cpus. The distance measures the costs to access the memory
of another NUMA-node. A NUMA-node has always a distance 10
to itself (lowest possible value).
The position in the array corresponds
with the NUMA-node ID.
Some work-loads benefit from pinning threads to a logical cpus. For instance
scheduling algorithm numa::work_stealing
pins the thread
that runs the fiber scheduler to a logical cpu. This prevents the operating
system scheduler to move the thread to another logical cpu that might run other
fiber scheduler(s) or migrating the thread to a logical cpu part of another
NUMA-node.
void thread( std::uint32_t cpu_id, std::uint32_t node_id, std::vector< boost::fibers::numa::node > const& topo) { // thread registers itself at work-stealing scheduler boost::fibers::use_scheduling_algorithm< boost::fibers::algo::numa::work_stealing >( cpu_id, node_id, topo); ... } // evaluate the NUMA topology std::vector< boost::fibers::numa::node > topo = boost::fibers::numa::topology(); // start-thread runs on NUMA-node `0` auto node = topo[0]; // start-thread is pinnded to first cpu ID in the list of logical cpus of NUMA-node `0` auto start_cpu_id = * node.logical_cpus.begin(); // start worker-threads first std::vector< std::thread > threads; for ( auto & node : topo) { for ( std::uint32_t cpu_id : node.logical_cpus) { // exclude start-thread if ( start_cpu_id != cpu_id) { // spawn thread threads.emplace_back( thread, cpu_id, node.id, std::cref( topo) ); } } } // start-thread registers itself on work-stealing scheduler boost::fibers::use_scheduling_algorithm< boost::fibers::algo::numa::work_stealing >( start_cpu_id, node.id, topo); ...
The example evaluates the NUMA topology with boost::fibers::numa::topology()
and spawns for each logical cpu a thread. Each spawned thread installs the
NUMA-aware work-stealing scheduler. The scheduler pins the thread to the logical
cpu that was specified at construction.
If the local queue of one thread
runs out of ready fibers, the thread tries to steal a ready fiber from another
thread running at logical cpu that belong to the same NUMA-node (local memory
access). If no fiber could be stolen, the thread tries to steal fibers from
logical cpus part of other NUMA-nodes (remote memory access).
#include <boost/fiber/numa/pin_thread.hpp> #include <boost/fiber/numa/topology.hpp> namespace boost { namespace fibers { namespace numa { struct node { std::uint32_t id; std::set< std::uint32_t > logical_cpus; std::vector< std::uint32_t > distance; }; bool operator<( node const&, node const&) noexcept; std::vector< node > topology(); void pin_thread( std::uint32_t); void pin_thread( std::uint32_t, std::thread::native_handle_type); }}} #include <boost/fiber/numa/algo/work_stealing.hpp> namespace boost { namespace fibers { namespace numa { namespace algo { class work_stealing; }}}
numa::node
#include <boost/fiber/numa/topology.hpp> namespace boost { namespace fibers { namespace numa { struct node { std::uint32_t id; std::set< std::uint32_t > logical_cpus; std::vector< std::uint32_t > distance; }; bool operator<( node const&, node const&) noexcept; }}}
id
std::uint32_t id;
ID of the NUMA-node
logical_cpus
std::set< std::uint32_t > logical_cpus;
set of logical cpu IDs belonging to the NUMA-node
distance
std::vector< std::uint32_t > distance;
The distance between NUMA-nodes describe the cots of accessing the remote memory.
A NUMA-node has a distance of 10
to itself, remote NUMA-nodes have a distance > 10
.
The index in the array corresponds to the ID id
of the NUMA-node. At the moment only Linux returns the correct distances,
for all other operating systems remote NUMA-nodes get a default value
of 20
.
operator<
()
bool operator<( node const& lhs, node const& rhs) const noexcept;
true
if lhs
!= rhs
is true and the implementation-defined total order of node::id
values places lhs
before
rhs
, false otherwise.
Nothing.
numa::topology()
#include <boost/fiber/numa/topology.hpp> namespace boost { namespace fibers { namespace numa { std::vector< node > topology(); }}}
Evaluates the NUMA topology.
a vector of NUMA-nodes describing the NUMA architecture of the system (each element represents a NUMA-node).
system_error
numa::pin_thread()
#include <boost/fiber/numa/pin_thread.hpp> namespace boost { namespace fibers { namespace numa { void pin_thread( std::uint32_t cpu_id); void pin_thread( std::uint32_t cpu_id, std::thread::native_handle_type h); }}}
First version pins this thread
to the logical cpu with ID
cpu_id
, e.g. the operating
system scheduler will not migrate the thread to another logical cpu.
The second variant pins the thread with the native ID h
to logical cpu with ID cpu_id
.
system_error
numa::work_stealing
This class implements algorithm
; the thread running this scheduler
is pinned to the given logical cpu. If the local ready-queue runs out of ready
fibers, ready fibers are stolen from other schedulers that run on logical cpus
that belong to the same NUMA-node (local memory access).
If no ready
fibers can be stolen from the local NUMA-node, the algorithm selects schedulers
running on other NUMA-nodes (remote memory access).
The victim scheduler
(from which a ready fiber is stolen) is selected at random.
#include <boost/fiber/numa/algo/work_stealing.hpp> namespace boost { namespace fibers { namespace numa { namespace algo { class work_stealing : public algorithm { public: work_stealing( std::uint32_t cpu_id, std::uint32_t node_id, std::vector< boost::fibers::numa::node > const& topo, bool suspend = false); work_stealing( work_stealing const&) = delete; work_stealing( work_stealing &&) = delete; work_stealing & operator=( work_stealing const&) = delete; work_stealing & operator=( work_stealing &&) = delete; virtual void awakened( context *) noexcept; virtual context * pick_next() noexcept; virtual bool has_ready_fibers() const noexcept; virtual void suspend_until( std::chrono::steady_clock::time_point const&) noexcept; virtual void notify() noexcept; }; }}}}
work_stealing( std::uint32_t cpu_id, std::uint32_t node_id, std::vector< boost::fibers::numa::node > const& topo, bool suspend = false);
Constructs work-stealing scheduling algorithm. The thread is pinned to
logical cpu with ID cpu_id
.
If local ready-queue runs out of ready fibers, ready fibers are stolen
from other schedulers using topology
(represents the NUMA-topology of the system).
system_error
If suspend
is set to
true
, then the scheduler
suspends if no ready fiber could be stolen. The scheduler will by woken
up if a sleeping fiber times out or it was notified from remote (other
thread or fiber scheduler).
awakened
()
virtual void awakened( context * f) noexcept;
Enqueues fiber f
onto
the shared ready queue.
Nothing.
pick_next
()
virtual context * pick_next() noexcept;
the fiber at the head of the ready queue, or nullptr
if the queue is empty.
Nothing.
Placing ready fibers onto the tail of the sahred queue, and returning them from the head of that queue, shares the thread between ready fibers in round-robin fashion.
has_ready_fibers
()
virtual bool has_ready_fibers() const noexcept;
true
if scheduler has fibers
ready to run.
Nothing.
suspend_until
()
virtual void suspend_until( std::chrono::steady_clock::time_point const& abs_time) noexcept;
Informs work_stealing
that no ready fiber will be available until time-point abs_time
. This implementation blocks
in std::condition_variable::wait_until()
.
Nothing.
notify
()
virtual void notify() noexcept = 0;
Wake up a pending call to work_stealing::suspend_until()
,
some fibers might be ready. This implementation wakes suspend_until()
via std::condition_variable::notify_all()
.
Nothing.
[8] On x86 the interconnection is implemented by Intel's Quick Path Interconnect (QPI) and AMD's HyperTransport.