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<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
<link rel="stylesheet" href="../common-revealjs/css/reveal.css">
<link rel="stylesheet" href="../common-revealjs/css/theme/white.css">
<link rel="stylesheet" href="../common-revealjs/css/custom.css">
<script>
// This is needed when printing the slides to pdf
var link = document.createElement( 'link' );
link.rel = 'stylesheet';
link.type = 'text/css';
link.href = window.location.search.match( /print-pdf/gi ) ? '../common-revealjs/css/print/pdf.css' : '../common-revealjs/css/print/paper.css';
document.getElementsByTagName( 'head' )[0].appendChild( link );
</script>
<script>
// This is used to display the static images on each slide,
// See global-images in this html file and custom.css
(function() {
if(window.addEventListener) {
window.addEventListener('load', () => {
let slides = document.getElementsByClassName("slide-background");
if (slides.length === 0) {
slides = document.getElementsByClassName("pdf-page")
}
// Insert global images on each slide
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})();
</script>
</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>
## Local memory
</div>
</section>
<!--Slide 2-->
<section class="hbox" data-markdown>
## Learning Objectives
* Learn about tiling using local memory
* Learn about how to synchronize work-groups
</section>
<!--Slide 3-->
<section>
<div class="hbox" data-markdown>
#### Cost of accessing global memory
</div>
<div class="container" data-markdown>
* As we covered earlier global memory is very expensive to access.
* Even with coalesced global memory access if you are accessing the same elements multiple times that can be expensive.
* Instead you want to cache those values in a lower latency memory.
</div>
</section>
<!--Slide 4-->
<section>
<div class="hbox" data-markdown>
#### Why are image convolutions good on a GPU?
</div>
<div class="container">
<div class="col" data-markdown>
</div>
<div class="col" data-markdown>
* Looking at the image convolution example.
* For each output pixel we are reading up to NxM pixels from the input image, where N and M are the dimensions of the filter.
* This means each input pixel is being read up to NxM times:
* 3x3 filter: up to 9 ops.
* 5x5 filter: up to 25 ops.
* 7x7 filter: up to 49 ops.
* If each of these operations is a separate load from global memory this becomes very expensive.
</div>
</div>
</section>
<!--Slide 5-->
<section>
<div class="hbox" data-markdown>
#### Using local memory
</div>
<div class="container" data-markdown>
</div>
<div class="container" data-markdown>
* The solution is local memory.
* Local memory is generally on-chip and doesn't have a cache as it's managed manually so is much lower latency.
* Local memory is a smaller dedicated region of memory per work-group.
* Local memory can be used to cache, allowing us to read from global memory just once and then read from local memory instead, often referred to as a scratchpad.
</div>
</section>
<!--Slide 6-->
<section>
<div class="hbox" data-markdown>
#### Tiling
</div>
<div class="container">
<div class="col" data-markdown>
</div>
<div class="col" data-markdown>
* The iteration space of the kernel function is mapped across multiple work-groups.
* Each work-group has it's own region of local memory.
* You want to split the input image data into tiles, one for each work-group.
</div>
</div>
</section>
<!--Slide 7-->
<section>
<div class="hbox" data-markdown>
#### Local accessors
</div>
<div class="container">
<code class="code-100pc"><pre>
auto scratchpad = sycl::accessor<int, 1, sycl::access::target::local>(sycl::range{workGroupSize}, cgh);
</code></pre>
</div>
<div class="container" data-markdown>
* Local memory is allocated via an `accessor` with the `access::target::local` access target.
* Unlike regular `accessor`s they are not created with a `buffer`, they allocate memory per work-group for the duration of the kernel function.
* The `range` provided is the number of elements of the specified type to allocate per work-group.
</div>
</section>
<!--Slide 8-->
<section>
<div class="hbox" data-markdown>
#### Synchronization
</div>
<div class="container" data-markdown>
* Local memory can be used to share partial results between work-items.
* When doing so it's important to synchronize between writes and read to memory to ensure all work-items have reached the same point in the program.
</div>
</section>
<!--Slide 9-->
<section>
<div class="hbox" data-markdown>
#### Synchronization
</div>
<div class="container">
<div class="col" data-markdown>
</div>
<div class="col" data-markdown>
* Remember that work-items are not guaranteed to all execute at the same time (in parallel).
</div>
</div>
</section>
<!--Slide 10-->
<section>
<div class="hbox" data-markdown>
#### Synchronization
</div>
<div class="container">
<div class="col" data-markdown>
</div>
<div class="col" data-markdown>
* A work-item can share results with other work-items via local (or global) memory.
</div>
</div>
</section>
<!--Slide 11-->
<section>
<div class="hbox" data-markdown>
#### Synchronization
</div>
<div class="container">
<div class="col" data-markdown>
</div>
<div class="col" data-markdown>
* This means it's possible for a work-item to read a result that hasn't been written to yet.
* This creates a data race.
</div>
</div>
</section>
<!--Slide 12-->
<section>
<div class="hbox" data-markdown>
#### Synchronization
</div>
<div class="container">
<div class="col" data-markdown>
</div>
<div class="col" data-markdown>
* This problem can be solved with a synchronization primitive called a work-group barrier.
</div>
</div>
</section>
<!--Slide 13-->
<section>
<div class="hbox" data-markdown>
#### Synchronization
</div>
<div class="container">
<div class="col" data-markdown>
</div>
<div class="col" data-markdown>
* When a work-group barrier is inserted work-items will wait until all work-items in the work-group have reached that point.
</div>
</div>
</section>
<!--Slide 14-->
<section>
<div class="hbox" data-markdown>
#### Synchronization
</div>
<div class="container">
<div class="col" data-markdown>
</div>
<div class="col" data-markdown>
* Only then can any work-items in the work-group continue execution.
</div>
</div>
</section>
<!--Slide 15-->
<section>
<div class="hbox" data-markdown>
#### Synchronization
</div>
<div class="container">
<div class="col" data-markdown>
</div>
<div class="col" data-markdown>
* So now you can be sure that all of the results that you want to read have been written to.
</div>
</div>
</section>
<!--Slide 16-->
<section>
<div class="hbox" data-markdown>
#### Synchronization
</div>
<div class="container">
<div class="col" data-markdown>
</div>
<div class="col" data-markdown>
* However note that this does not apply across work-group boundaries.
* So if you write in a work-item of one work-group and then read it in a work-item of another work-group you again have a data race.
* Furthermore, remember that work-items can only access their own local memory and not that of any other work-groups.
</div>
</div>
</section>
<!--Slide 17-->
<section>
<div class="hbox" data-markdown>
#### Group_barrier
</div>
<div class="container">
<code class="code-100pc"><pre>
sycl::group_barrier(item.get_group());
</code></pre>
</div>
<div class="container" data-markdown>
* Work-group barriers can be invoked by calling `group_barrier` and passing a `group` object.
* You can retrieve a `group` object representing the current work-group by calling `get_group` on an `nd_item`.
* Note this requires the `nd_range` variant of `parallel_for`.
</div>
</section>
<!--Slide 18-->
<section>
<div class="hbox" data-markdown>
#### Local memory image convolution performance
</div>
<div class="container"data-markdown>
</div>
</section>
<!--Slide 11-->
<section>
<div class="hbox" data-markdown>
## Questions
</div>
</section>
<!--Slide 12-->
<section>
<div class="hbox" data-markdown>
#### Exercise
</div>
<div class="container" data-markdown>
Code_Exercises/Exercise_18_Local_Memory_Tiling/source
</div>
<div class="container" data-markdown>
Use local memory to cache a tile of the input image data per work-group.
</div>
</section>
</div>
</div>
<script src="../common-revealjs/js/reveal.js"></script>
<script src="../common-revealjs/plugin/markdown/marked.js"></script>
<script src="../common-revealjs/plugin/markdown/markdown.js"></script>
<script src="../common-revealjs/plugin/notes/notes.js"></script>
<script>
Reveal.initialize({mouseWheel: true, defaultNotes: true});
Reveal.configure({ slideNumber: true });
</script>
</body>
</html>