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Previous: Introduction to HPX - Part 1
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A C++ Standard Library for Concurrency and Parallelism
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Solidly based on a theoretical foundation – a well defined, new execution model
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Exposes a coherent and uniform, standards-oriented API for ease of programming parallel and distributed applications.
- Enables to write fully asynchronous code using hundreds of millions of threads.
- Provides unified syntax and semantics for local and remote operations.
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Developed to run at any scale
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Compliant C++ Standard implementation (and more)
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Open Source: Published under the Boost Software License
HPX represents an innovative mixture of
- A global system-wide address space (AGAS - Active Global Address Space)
- Fine grain parallelism and lightweight synchronization
- Combined with implicit, work queue based, message driven computation
- Full semantic equivalence of local and remote execution, and
- Explicit support for hardware accelerators
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Widely portable
- Platforms: x86/64, Xeon/Phi, ARM 32/64, Power, BlueGene/Q
- Operating systems: Linux, Windows, Android, OS/X
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Well integrated with compiler’s C++ Standard libraries
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Enables writing applications which out-perform and out-scale existing applications based on OpenMP/MPI
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Is published under Boost license and has an open, active, and thriving developer community.
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Can be used as a platform for research and experimentation
| Class | Description |
|---|---|
hpx::thread |
Low level thread of control |
hpx::mutex |
Low level synchronization facility |
hpx::lcos::local::condition_variable |
Signal a condition |
hpx::future |
Asynchronous result transport (receiving end) |
hpx::promise, hpx::lcos::local::promise |
Asynchronous result transport (producing end) |
hpx::lcos::packaged_task |
Asynchronous result transport (producing end) |
hpx::async |
Spawning tasks (returns a future) |
hpx::bind |
Binding Parameters to callables |
hpx::function |
Type erased function object |
hpx::tuple |
Tuple |
hpx::any |
Type erased object (similar to void*) |
hpx::parallel::for_each, etc. |
Parallel Algorithms |
hpx::compute::vector |
Continous storage for N elements, also GPU |
Extensions to the standards APIs where necessary, maintaining full compatibility.
- Objects to synchronize between different threads of execution.
- Ability to suspend and reactivate tasks
- Examples:
mutexcondition_variablechannelpromisepackaged_taskfuturewhen_all,when_any,wait_all,wait_any- ...
- More on those later
A future is an object representing a result which has not been calculated yet
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]
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- Enables transparent synchronization with producer
- Hides notion of dealing with threads
- Makes asynchrony manageable
- Allows for composition of several asynchronous operations
- Turns concurrency into parallelism ]
Many ways to get hold of a future, simplest way is to use (std) async:
int universal_answer()
{
return 42;
}
--
void deep_thought()
{
future<int> promised_answer = async(&universal_answer);
// do other things for 7.5 million years
cout << promised_answer.get() << endl; // prints 42
}
template <typename R>
class future
{
// future constructors
// Query the state
// Waiting on the result
};
--
template <typename R>
class shared_future
{
// Future constructors
// Query the state
// Waiting on the result
};
Constructing a hpx::future<R>
template <typename R>
class future
{
// Future constructors
// Construct an empty future.
future();
// Move a future to a new one
future(future<R>&& f);
// Unwrap a future. The new future becomes ready when
// the inner, and outer futures are ready.
explicit future(future<future<R>>&& f);
explicit future(future<shared_future<R>>&& f);
// Turn this future into a shared_future. Invalidates the future!
shared_future<R> share();
// Query the state
// Waiting on the result
};
Querying the state of the future
template <typename R>
class future
{
// Future constructors
// Query the state
// Check if the future is ready yet.
bool is_ready();
// Check if the future has a value
bool has_value();
// Check if the future has an exception
bool has_exception();
// Waiting on the result
};
Waiting for the future to become ready
template <typename R>
class future
{
// Future constructors, Query the state...
// Waiting on the result
void wait() const;
// Waiting for the result, but not longer than until given time point
template <typename Clock, typename Duriation>
future_status wait_until(
std::chrono::time_point<Clock, Duration> const& abs_time) const;
// Waiting for the result, but not longer than give duration
template <typename Rep, typename Period>
future_status wait_for(
std::chrono::duration<Rep, Period> const& rel_time) const;
// Get the result...
};
Waiting for the future to become ready
template <typename R>
class future
{
// Future constructors, Query the state...
// Waiting on the result...
// Get the result. This function might block if the result has
// not been computed yet. Invalidates the future!
R get();
// Attach a continuation. The function f gets called with
// the (ready) future. Returns a new future with the result of
// the invocation of f. Invalidates the future!
template <typename F>
auto then(F&& f);
};
Constructing a hpx::shared_future<R>
template <typename R>
class shared_future
{
// Future constructors
// Construct an empty future.
shared_future();
// Move a future to a new one
shared_future(shared_future<R>&& f);
// Share ownership between two futures
shared_future(shared_future<R> const& f);
// Unwrap a future. The new future becomes ready when
// the inner, and outer future are ready.
explicit shared_future(shared_future<future<R>>&& f);
// implicitly share a future
shared_future(future<R>&& f);
// Query the state
// Waiting on the result
};
Waiting for the future to become ready
template <typename R>
class shared_future
{
// Future constructors
// Query the state
// Waiting on the result
void wait() const;
// Get the result. This function might block if the result has
// not been computed yet.
R const& get();
// Attach a continuation. The function f gets called with
// the (ready) future. Returns a new future with the result of
// the invocation of f.
template <typename F>
auto then(F&& f) const;
};
template <typename F, typename... Ts>
auto async(F&& f, Ts&&... ts)
-> future<decltype(f(std::forward<Ts>(ts)...)>;
Fis anything callable with the passed arguments (actions are callable)
--
template <typename F, typename... Ts>
auto async(launch_policy, F&& f, Ts&&... ts)
-> future<decltype(f(std::forward<Ts>(ts)...)>;
launch_policycan beasync,sync,fork,deferred
--
template <typename Executor typename F, typename... Ts>
auto async(Executor&&, F&& f, Ts&&... ts)
-> future<decltpype(f(std::forward<Ts>(ts)...)>;
Executoris a concept to be introduced later
hpx::lcos::local::promise<int> p; // local only
hpx::future<int> f = p.get_future();
// f.is_ready() == false, f.get(); would lead to a deadlock
p.set_value(42);
// Print 42
std::cout << f.get() << std::endl;
hpx::promise<int> p; // globally visible
hpx::future<int> f = p.get_future();
// f.is_ready() == false, f.get(); would lead to a deadlock
hpx::async(
[](hpx::id_type promise_id)
{
hpx::set_lco_value(promise_id, 42);
}
, p.get_id());
// Print 42
std::cout << f.get() << std::endl;
- Producing futures that are ready at construction
template <typename T>
future<typename decay<T>::type> make_ready_future(T&& t);
future<void> make_ready_future();
- Futures are the main interface to express asynchrony
- Most API functions in HPX return futures
- This was just an excerpt ... let's see more!
future<int> f1 = hpx::async(...);
// Call continuation once f1 is ready. f2 will become ready once
// the continuation has been run.
future<double> f2 = f1.then(
[](future<int>&& f) { return f.get() + 0.0; });
--
- The continuation needs to take the future as parameter to allow
for exception handling. Exceptions happening in asynchronous calls
will get rethrown on
.get() thenaccepts launch policies as well as executorsf1will get invalidated.
--
No invalidation:
shared_future<int> f1 = hpx::async(...);
// Call continuation once f1 is ready. f2 will become ready once
// the continuation has been run.
future<double> f2 = f1.then(
[](future<int>&& f) { return f.get() + 0.0; });
future<int> f1 = hpx::async(...);
future<std::string> f2 = hpx::async(...);
auto all_f = hpx::when_all(f1, f2);
future<std::vector<float>> result =
all_f.then(
[](auto f) -> std::vector<float>
{
// ...
});
--
- Allows for attaching continuations to more than one future
f1andf2will get invalidated. (Useshared_futureif you need them afterwards)- Also accepts a
std::vector<future<T>>or a pair of iterators
std::vector<future<int>> fs = ...;
future<int> fi =
hpx::when_any(fs).then(
[](auto f)
{
auto res = f.get();
return res.futures[res.index];
});
- Allows for waiting on any of the input futures
- Returns a
future<when_any_result<Sequence>>:
--
template <typename Sequence>
struct when_any_result
{
std::size_t index; // Index to a future that became ready
Sequence futures; // Sequence of futures
};
- Shortcut to
when_all(...).then(...)
future<int> f1 = hpx::async(...);
future<std::string> f2 = hpx::async(...);
future<double> f3 =
hpx::dataflow(
[](future<int>, future<std::string>) -> double
{
// ...
},
std::move(f1), std::move(f2));
--
- Calls the passed function after all arguments that were futures have become ready
- Returns a future that becomes ready once the function has finished execution
- Accepts launch policies as well as executors as the first parameter
- Data parallelism
- Parallel Algorithms
- GPUs
- Task based asynchronous and continuation style parallelism
future<R>,async, etc...
- General fork/join style parallelism
- Instruction Level Parallelism
- SIMD instructions
- The execution restrictions applicable for work items ('safe to be executed concurrently')
- In what sequence work items have to be executed
- Where work items should be executed (CPU, NUMA domain, Core, GPU)
- The parameters of the execution environment (chunk sizes, etc.)
- Specify execution guarantees (in terms of thread-safety) for executed parallel tasks:
execution::sequenced_policy: seqexecution::parallel_policy: parexecution::unsequenced_policy: unseq
--
- Asynchronous Execution Policies:
execution::sequenced_task_policy: seq(task)execution::parallel_task_policy: par(task)
- In both cases the formerly synchronous functions return a
future<R> - Instruct the parallel construct to be executed asynchronously
- Allows integration with asynchronous control flow
name: executors
- Executors are objects responsible for
- Creating execution agents on which work is performed (N4466)
- In N4466 this is limited to parallel algorithms, here much broader use
- Abstraction of the (potentially platform-specific) mechanisms for launching work
- Responsible for defining the Where and How of the execution of tasks
- Executors must implement one function:
async_execute(F&& f, Args&&... args)
- Invocation of executors happens through
executor_traitswhich exposes (emulates) additional functionality:
executor_traits<my_executor_type>::async_execute(
my_executor,
[](size_t i){ // perform task i }, n);
- Four modes of invocation: single async, single sync, bulk async and bulk sync
- The async calls return a future
sequenced_executor,parallel_executor:- Default executors corresponding to par, seq
this_thread_executorthread_pool_executor- Specify core(s) to run on (NUMA aware)
distribution_policy_executor- Use one of HPX’s (distributed) distribution policies, specify node(s) to run on
hpx::compute::host::block_executor- Use a set of CPUs
hpx::compute::cuda::default_executor- Use for running things on GPU
- Etc.
- Allow to control the grain size of work
- i.e. amount of iterations of a parallel
for_eachrun on the same thread - Similar to OpenMP scheduling policies:
static,guided,dynamic - Much more fine control
- i.e. amount of iterations of a parallel
Execution policies have associated default executor and default executor parameters
par: parallel executor, static chunk sizeseq: sequenced executor, no chunking- Rebind executor and executor parameters
// rebind only executor
numa_executor exec;
auto policy1 = par.on(exec);
--
// rebind only executor parameter
static_chunk_size param;
auto policy2 = par.with(param);
--
// rebind both
auto policy3 = par.on(exec).with(param);
- Mechanism to tell where to allocate data
- C++ defines an Allocator concept
std::allocator<T> - Extensions:
- Where do you want to allocate Data
- Ability to bulk allocate Data (NUMA aware allocation, GPU Device Allocation)
- Data Structures to use those allocators
- Different strategies for different platforms
- Need interface to control explicit placement of data
- NUMA architectures
- GPUs
- Distributed systems
- Need interface to control explicit placement of data
hpx::compute::vector<T, Allocator>- Same interface as
std::vector<T> - Manages data locality through allocator
- Uses execution target objects for data placement
- Allows for direct manipulation of data on NUMA domains, GPUs, remote nodes, etc.
- Same interface as
hpx::partitioned_vector<T>- Same interface as
std::vector<T>(almost) - Segmented data store
- Segments can be
hpx::compute::vector<T> - Uses distribution_policy for data placement
- Allows for manipulation of data on several targets
- Same interface as
- Opaque type which represent a place in the system
- Used to identify data placement
- Used to specify execution site close to data
- Targets encapsulate architecture specifics
- CPU sets (NUMA domains), Scratch Pad Memory, GPU devices, Remote nodes
- Allocators to be initialized from targets
- Customization of data placement
- Executors to be initialized from targets as well
- Make sure code is executed close to placed data
Goal: SAXPY routine with data locality
a[i] = b[i] * x + c[i]forifrom0toN-1- Using parallel algorithms
- Explicit control over data locality
- No raw loops
Step 1: Writing the serial version
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std::vector<double> a = ...;
std::vector<double> b = ...;
std::vector<double> c = ...;
double x = ...;
std::transform(
b.begin(), b.end(), c.begin(),
a.begin(),
[x](double bb, double cc)
{
* return bb * x + cc;
}
);
]
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bbisb[i]ccisc[i]- the calculated value gets written to
a[i] - Complete code ]
Step 2: Parallelize it
.left-column[
std::vector<double> a = ...;
std::vector<double> b = ...;
std::vector<double> c = ...;
double x = ...;
*hpx::parallel::transform(
* hpx::parallel::execution::par,
b.begin(), b.end(), c.begin(),
a.begin(),
[x](double bb, double cc)
{
return bb * x + cc;
}
);
]
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- Replace the standard algorithm with a parallel one
- Set parallel exeuction policy
- Complete code ]
Step 3: Adding data locality
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using hpx::compute::host;
typedef block_executor<> executor;
typedef block_allocator<double>
allocator;
auto numa_domains = numa_nodes();
executor exec(numa_domains);
allocator alloc(numa_domains);
using hpx::compute::vector;
vector<double, allocator> a = ...;
vector<double, allocator> b = ...;
vector<double, allocator> c = ...;
double x = ...;
]
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*using hpx::parallel::execution::par;
*auto policy = par.on(exec);
hpx::parallel::transform(policy,
b.begin(), b.end(), c.begin(),
a.begin(),
[x](double bb, double cc)
{
return bb * x + cc;
}
);
- Get targets for locality of data and execution
- Setup Executor and Allocator
- Run on the allocator
- Complete code ]
Optional Step: Running it on the GPU
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using hpx::compute::cuda;
typedef default_executor<> executor;
typedef allocator<double> allocator;
target device("K40");
executor exec(device);
allocator alloc(device);
using hpx::compute::vector;
vector<double, allocator> a = ...;
vector<double, allocator> b = ...;
vector<double, allocator> c = ...;
double x = ...;
] .right-column[
*using hpx::parallel::execution::par;
*auto policy = par.on(exec);
hpx::parallel::transform(policy,
b.begin(), b.end(), c.begin(),
a.begin(),
[x](double bb, double cc)
{
return bb * x + cc;
}
);
- Get targets for locality of data and execution
- Setup Executor and Allocator
- Run on the allocator
- Complete code
- Works only for CUDA version 8 :( ]
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