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<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
<link rel="stylesheet" href="../common-revealjs/css/reveal.css">
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<style>
/* Custom styling for the highlighted code */
.highlight {
color: red;
font-weight: bold;
}
</style>
</head>
<body>
<div class="reveal">
<div class="slides">
<div id="global-images" class="global-images">
<img src="../common-revealjs/images/sycl_academy.png" />
<img src="../common-revealjs/images/sycl_logo.png" />
<img src="../common-revealjs/images/trademarks.png" />
<img src="../common-revealjs/images/codeplay.png" />
</div>
<!--Slide 1-->
<section class="hbox">
<div class="hbox" data-markdown>
## More SYCL Features
</div>
</section>
<!--Slide 2-->
<section class="hbox" data-markdown>
## Learning Objectives
* Learn about atomic operations and how to use them in SYCL kernels
* Learn about SYCL group algorithms
* Learn about SYCL reductions
</section>
<!--Slide 3-->
<section>
<div class="hbox" data-markdown>
#### Race Condition
</div>
<div class="container">
<div class="col" data-markdown>
* In a multithreaded environment, multiple work items writing indiscriminately
to the same area of memory causes a race condition.
</div>
<div class="col" data-markdown>
```
q.parallel_for([=](sycl::item<1> it) {
// Race condition! Multiple threads
// writing to same area of memory
a[0] = it.get_global_linear_id();
});
```
</div>
</div>
</section>
<!--Slide 4-->
<section>
<div class="hbox" data-markdown>
#### Race Condition
</div>
<div class="container">
<div class="col" data-markdown>
```
q.parallel_for([=](sycl::item<1> it) {
// Race condition! Multiple threads
// writing to same area of memory
a[0] = it.get_global_linear_id();
});
```
</div>
<div class="col" data-markdown>
* SYCL does not guarantee any particular ordering for the execution of work
items.
* When multiple work items concurrently write different values to the same area
of memory, there is no way of knowing which value will be held in memory
once all work items have finished writing.
* This is called a race condition and can be a source of non determinism in
code execution.
</div>
</div>
</section>
<!--Slide 5-->
<section>
<div class="hbox" data-markdown>
#### Atomic Operations
</div>
<div class="container">
<div class="col" data-markdown>
```
q.parallel_for([=](sycl::item<1> it) {
sycl::atomic_ref<T,
sycl::memory_order_relaxed,
sycl::memory_scope_device>
(a[0])
.fetch_add(it.get_global_linear_id());
});
```
</div>
<div class="col" data-markdown>
* Atomic operations are needed in order to deterministically combine values
from different work items.
* Atomic operations enforce a particular ordering of instructions across work
items. Some orderings include `memory_order_relaxed`, `memory_order_acq_rel`,
`memory_order_seq_cst`.
</div>
</div>
</section>
<!--Slide 6 -->
<section>
<div class="hbox" data-markdown>
#### Atomic Operations
</div>
<div class="container">
<div class="col" data-markdown>
```
q.parallel_for([=](sycl::item<1> it) {
sycl::atomic_ref<T,
sycl::memory_order_relaxed,
sycl::memory_scope_device>
(a[0])
.fetch_add(it.get_global_linear_id());
});
```
</div>
<div class="col" data-markdown>
* Atomic operations are needed in order to deterministically combine values
from different work items.
* Atomic operations enforce a particular ordering of instructions across work
items. Some orderings include `memory_order_relaxed`, `memory_order_acq_rel`,
`memory_order_seq_cst`.
</div>
</div>
</section>
<!--Slide 7 -->
<section>
<div class="hbox" data-markdown>
#### Atomic Operations
</div>
<div class="container">
<div class="col" data-markdown>
```
q.parallel_for([=](sycl::item<1> it) {
sycl::atomic_ref<T,
sycl::memory_order_relaxed,
sycl::memory_scope_device>
(a[0])
.fetch_add(it.get_global_linear_id());
});
```
</div>
<div class="col" data-markdown>
* Atomic operations are needed in order to deterministically combine values
from different work items.
* Atomic operations enforce a particular ordering of instructions across work
items. Some orderings include `memory_order_relaxed`, `memory_order_acq_rel`,
`memory_order_seq_cst`.
</div>
</div>
</section>
<!--Slide 8 -->
<section>
<div class="hbox" data-markdown>
#### Atomic Operations
</div>
<div class="container">
<div class="col" data-markdown>
```
q.parallel_for([=](sycl::item<1> it) {
sycl::atomic_ref<T,
sycl::memory_order_relaxed,
sycl::memory_scope_device>
(a[0])
.fetch_add(it.get_global_linear_id());
});
```
</div>
<div class="col" data-markdown>
* Atomic operations are needed in order to deterministically combine values
from different work items.
* Atomic operations enforce a particular ordering of instructions across work
items. Some orderings include `memory_order_relaxed`, `memory_order_acq_rel`,
`memory_order_seq_cst`.
</div>
</div>
</section>
<!--Slide 9 -->
<section>
<div class="hbox" data-markdown>
#### Atomic Operations
</div>
<div class="container">
<div class="col" data-markdown>
```
q.parallel_for([=](sycl::item<1> it) {
sycl::atomic_ref<T,
sycl::memory_order_relaxed,
sycl::memory_scope_device>
(a[0])
.fetch_add(it.get_global_linear_id());
});
```
</div>
<div class="col" data-markdown>
* Using atomics, values can be combined across work items without data races.
* `fetch_add`, `fetch_sub`, `fetch_max`, `fetch_min` are some of the ways we can
combine values atomically.
* `fetch_and` and `fetch_or` can also be used for
integral types
* Please see the SYCL Specification for more details.
</div>
</div>
</section>
<!--Slide 10 -->
<section>
<div class="hbox" data-markdown>
#### Atomic Operations
</div>
<div class="container">
<div class="col">
<pre><code>
q.parallel_for([=](sycl::item<1> it) {
sycl::atomic_ref<T,
sycl::memory_order_relaxed,
sycl::memory_scope_<span class="highlight">work_group</span>,
<span class="highlight">sycl::access::address_space::local_space</span>>
(a[0])
.fetch_add(it.get_global_linear_id());
});
</code></pre>
</div>
<div class="col" data-markdown>
* We can also specify the memory space of `a[0]`.
* If `a[0]` is in local memory, we should expect a speedup for using the local
memory atomic over the default atomic (which uses the generic address space).
</div>
</div>
</section>
<!--Slide 11 -->
<section>
<div class="hbox" data-markdown>
#### Group Algorithms
</div>
<div class="container" data-markdown>
* SYCL provides group algorithms which perform common operations over a single
workgroup.
* `reduce` algorithms perform fast work group reduction operation for some op
such as `plus`, `max`, etc.
* `any_of`, `all_of`, `none_of` as well as the `joint_*` counterparts perform
some predicate checking and return the same value to all items in a work
group.
* `permute_group_by_xor` permutes values among work items in a work group,
according to a provided mask.
* `inclusive_scan` and `exclusive_scan`. For a scan of elements: `[x0,...,xn]`
the ith result of an exclusive scan is the combination of the elements
[x0,...,x{i-1}] and the ith result of an inclusive scan is the combination of
elements [x0,...,xi] using some binary op.
* Please see the SYCL specification for more details.
</div>
</section>
<!--Slide 12 -->
<section>
<div class="hbox" data-markdown>
#### Group Algorithms
</div>
<div class="container" data-markdown>
* Group algorithms can operate on different group scopes, such as `work_group`,
`sub_group`.
* All work items in a given group scope must call the function in convergent
control flow.
* Please see the SYCL specification for more details.
</div>
</section>
</section>
<!--Slide 13 -->
<section>
<div class="hbox" data-markdown>
#### SYCL Reductions
</div>
<div class="container">
<div class="col">
<pre><code>
q.submit([&](sycl::handler &cgh) {
// Output of reduction will be in ptr
auto sumReduction = sycl::reduction(ptr,
sycl::plus<T>());
cgh.parallel_for(myNd, <span class="highlight">sumReduction</span>,
[=](sycl::nd_item<1> item, <span class="highlight">auto &sum</span>) {
sum += devA[item.get_global_linear_id()];
});
});
</code></pre>
</div>
<div class="col" data-markdown>
* SYCL provides reduction operators to perform fast reductions using some binary
operation.
* Reductions can be performed with `sycl::plus`, `sycl::maximum`, `sycl::multiplies` etc.
* Please see the SYCL specification for more details.
</div>
</div>
</section>
<!--Slide 14 -->
<section>
<div class="hbox" data-markdown>
#### SYCL Reductions
</div>
<div class="container">
<div class="col">
<pre><code>
q.submit([&](sycl::handler &cgh) {
// Output of reduction will be in ptr
auto maxReduction = sycl::reduction(ptr,
<span class="highlight">sycl::maximum</span><T>());
cgh.parallel_for(myNd, maxReduction,
[=](sycl::nd_item<1> item, <span class="highlight">auto &myMax</span>) {
myMax.<span class="highlight">combine</span>(devA[item.get_global_linear_id()]);
});
});
</code></pre>
</div>
<div class="col" data-markdown>
* SYCL provides reduction operators to perform fast reductions using some binary
operation.
* Reductions can be performed with `sycl::plus`, `sycl::maximum`,
`sycl::multiplies` etc.
* Please see the SYCL specification for more details.
</div>
</div>
</section>
<!--Slide 15-->
<section>
<div class="hbox" data-markdown>
## Questions
</div>
</section>
<!--Slide 16 -->
<section>
<div class="hbox" data-markdown>
#### Exercise
* See how atomics, group algorithms and `sycl::reductions` are used in
implementing a simple reduction operation.
* Which code runs fastest? Which code is simplest?
</div>
</section>
</div>
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