<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Gpu on QSysArch - Quantum Computer System Architecture</title><link>https://qsysarch.com/it/categories/gpu/</link><description>Recent content in Gpu on QSysArch - Quantum Computer System Architecture</description><generator>Hugo</generator><language>it</language><lastBuildDate>Sun, 28 Jun 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://qsysarch.com/it/categories/gpu/index.xml" rel="self" type="application/rss+xml"/><item><title>xPU: Microarchitetture per l'accelerazione hardware dell'inferenza AI</title><link>https://qsysarch.com/it/posts/ai-inference-hardware-acceleration-architecture/</link><pubDate>Sun, 28 Jun 2026 00:00:00 +0000</pubDate><guid>https://qsysarch.com/it/posts/ai-inference-hardware-acceleration-architecture/</guid><description>&lt;p&gt;xPU: è un termine generico che indica un&amp;rsquo;unità di elaborazione (PU) specializzata, dove &amp;ldquo;x&amp;rdquo; può rappresentare qualsiasi architettura di calcolo personalizzata per uno specifico carico di lavoro. Le varianti più comuni includono GPU (grafica), TPU (tensoriali/IA), NPU (neurali), DPU (dati) o PPU (a impulsi, utilizzate principalmente per gli stack di controllo quantistico).&lt;/p&gt;&#10;&lt;p&gt;&#10;&lt;img class="glightbox" src="https://qsysarch.com/images/gpxpus/xpu-system-architecture.webp" alt="XPU: Architettura di sistema di coprocessamento specializzato" /&gt;&lt;/p&gt;&#10;&lt;p&gt;Questo brevissimo promemoria si propone di fornire spunti di riflessione sul panorama del mercato xPU applicato all&amp;rsquo;inferenza dell&amp;rsquo;IA.&lt;/p&gt;</description></item><item><title>Kernel di moltiplicazione matriciale generale (GEMM)</title><link>https://qsysarch.com/it/posts/gemm-kernels/</link><pubDate>Sat, 28 Mar 2026 00:00:00 +0000</pubDate><guid>https://qsysarch.com/it/posts/gemm-kernels/</guid><description>&lt;p&gt;Questo promemoria riprende i concetti di base delle GPU. Si concentra sull&amp;rsquo;operazione fondamentale del deep learning: la moltiplicazione di matrici. In primo luogo, descriverò l&amp;rsquo;implementazione di base e accademica. Successivamente, esaminerò le ottimizzazioni necessarie per utilizzare la GPU in modo efficiente.&lt;/p&gt;&#10;&lt;h1 id="comprensione-del-moltiplicatore-di-matrici-ingenuo"&gt;&#10; Comprensione del moltiplicatore di matrici ingenuo&#10; &lt;a class="heading-link" href="#comprensione-del-moltiplicatore-di-matrici-ingenuo"&gt;&#10; &lt;i class="fa-solid fa-link" aria-hidden="true" title="Link to heading"&gt;&lt;/i&gt;&#10; &lt;span class="sr-only"&gt;Link to heading&lt;/span&gt;&#10; &lt;/a&gt;&#10;&lt;/h1&gt;&#10;&lt;p&gt;Iniziamo con questo semplice esempio che si trova spesso nei libri di testo.&lt;/p&gt;&#10;&lt;div class="code-block"&gt;&#10; &lt;div class="code-header"&gt; &lt;span data-label-text="Cpp"&gt;&lt;i class="fas fa-code fa-fw small"&gt;&lt;/i&gt;&lt;/span&gt; &lt;button aria-label="copy" data-title-succeed="Copied!"&gt;&lt;i class="far fa-clipboard"&gt;&lt;/i&gt;&lt;/button&gt;&lt;/div&gt;&#10; &#10; &lt;div class="highlight"&gt;&lt;pre tabindex="0" class="chroma"&gt;&lt;code class="language-cpp" data-lang="cpp"&gt;&lt;span class="line"&gt;&lt;span class="cl"&gt;&lt;span class="cm"&gt;/**&#10;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span class="line"&gt;&lt;span class="cl"&gt;&lt;span class="cm"&gt; * Kernel to perform matrix multiplication C = A × B&#10;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span class="line"&gt;&lt;span class="cl"&gt;&lt;span class="cm"&gt; * A is of size N x M, B is M x N, and C is N x N&#10;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span class="line"&gt;&lt;span class="cl"&gt;&lt;span class="cm"&gt; * Each thread computes one element of matrix C.&#10;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span class="line"&gt;&lt;span class="cl"&gt;&lt;span class="cm"&gt; */&lt;/span&gt;&#10;&lt;/span&gt;&lt;/span&gt;&lt;span class="line"&gt;&lt;span class="cl"&gt;&#10;&lt;/span&gt;&lt;/span&gt;&lt;span class="line"&gt;&lt;span class="cl"&gt;&lt;span class="n"&gt;__global__&lt;/span&gt; &lt;span class="kt"&gt;void&lt;/span&gt; &lt;span class="nf"&gt;naive_matrix_multiply&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="kt"&gt;float&lt;/span&gt;&lt;span class="o"&gt;*&lt;/span&gt; &lt;span class="n"&gt;A&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="kt"&gt;float&lt;/span&gt;&lt;span class="o"&gt;*&lt;/span&gt; &lt;span class="n"&gt;B&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="kt"&gt;float&lt;/span&gt;&lt;span class="o"&gt;*&lt;/span&gt; &lt;span class="n"&gt;C&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt; &lt;span class="p"&gt;{&lt;/span&gt;&#10;&lt;/span&gt;&lt;/span&gt;&lt;span class="line"&gt;&lt;span class="cl"&gt;&#10;&lt;/span&gt;&lt;/span&gt;&lt;span class="line"&gt;&lt;span class="cl"&gt; &lt;span class="c1"&gt;// Calculate global row and column for this thread&#10;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span class="line"&gt;&lt;span class="cl"&gt; &lt;span class="kt"&gt;int&lt;/span&gt; &lt;span class="n"&gt;row&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;blockIdx&lt;/span&gt;&lt;span class="p"&gt;.&lt;/span&gt;&lt;span class="n"&gt;y&lt;/span&gt; &lt;span class="cm"&gt;/* 0...16 */&lt;/span&gt; &lt;span class="o"&gt;*&lt;/span&gt; &lt;span class="n"&gt;blockDim&lt;/span&gt;&lt;span class="p"&gt;.&lt;/span&gt;&lt;span class="n"&gt;y&lt;/span&gt; &lt;span class="o"&gt;+&lt;/span&gt; &lt;span class="n"&gt;threadIdx&lt;/span&gt;&lt;span class="p"&gt;.&lt;/span&gt;&lt;span class="n"&gt;y&lt;/span&gt;&lt;span class="p"&gt;;&lt;/span&gt;&#10;&lt;/span&gt;&lt;/span&gt;&lt;span class="line"&gt;&lt;span class="cl"&gt; &lt;span class="kt"&gt;int&lt;/span&gt; &lt;span class="n"&gt;col&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;blockIdx&lt;/span&gt;&lt;span class="p"&gt;.&lt;/span&gt;&lt;span class="n"&gt;x&lt;/span&gt; &lt;span class="cm"&gt;/* 0...16 */&lt;/span&gt; &lt;span class="o"&gt;*&lt;/span&gt; &lt;span class="n"&gt;blockDim&lt;/span&gt;&lt;span class="p"&gt;.&lt;/span&gt;&lt;span class="n"&gt;x&lt;/span&gt; &lt;span class="o"&gt;+&lt;/span&gt; &lt;span class="n"&gt;threadIdx&lt;/span&gt;&lt;span class="p"&gt;.&lt;/span&gt;&lt;span class="n"&gt;x&lt;/span&gt;&lt;span class="p"&gt;;&lt;/span&gt;&#10;&lt;/span&gt;&lt;/span&gt;&lt;span class="line"&gt;&lt;span class="cl"&gt;&#10;&lt;/span&gt;&lt;/span&gt;&lt;span class="line"&gt;&lt;span class="cl"&gt; &lt;span class="k"&gt;if&lt;/span&gt; &lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;row&lt;/span&gt; &lt;span class="o"&gt;&amp;lt;&lt;/span&gt; &lt;span class="n"&gt;N&lt;/span&gt; &lt;span class="o"&gt;&amp;amp;&amp;amp;&lt;/span&gt; &lt;span class="n"&gt;col&lt;/span&gt; &lt;span class="o"&gt;&amp;lt;&lt;/span&gt; &lt;span class="n"&gt;N&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt; &lt;span class="p"&gt;{&lt;/span&gt;&#10;&lt;/span&gt;&lt;/span&gt;&lt;span class="line"&gt;&lt;span class="cl"&gt; &lt;span class="kt"&gt;float&lt;/span&gt; &lt;span class="n"&gt;sum&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="mf"&gt;0.0f&lt;/span&gt;&lt;span class="p"&gt;;&lt;/span&gt;&#10;&lt;/span&gt;&lt;/span&gt;&lt;span class="line"&gt;&lt;span class="cl"&gt; &lt;span class="k"&gt;for&lt;/span&gt; &lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="kt"&gt;int&lt;/span&gt; &lt;span class="n"&gt;k&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="mi"&gt;0&lt;/span&gt;&lt;span class="p"&gt;;&lt;/span&gt; &lt;span class="n"&gt;k&lt;/span&gt; &lt;span class="o"&gt;&amp;lt;&lt;/span&gt; &lt;span class="n"&gt;M&lt;/span&gt;&lt;span class="p"&gt;;&lt;/span&gt; &lt;span class="n"&gt;k&lt;/span&gt;&lt;span class="o"&gt;++&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt; &lt;span class="p"&gt;{&lt;/span&gt;&#10;&lt;/span&gt;&lt;/span&gt;&lt;span class="line"&gt;&lt;span class="cl"&gt; &lt;span class="c1"&gt;// C[row][col] = sum(A[row][i] * B[i][col])&#10;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span class="line"&gt;&lt;span class="cl"&gt; &lt;span class="n"&gt;sum&lt;/span&gt; &lt;span class="o"&gt;+=&lt;/span&gt; &lt;span class="n"&gt;A&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="n"&gt;row&lt;/span&gt; &lt;span class="o"&gt;*&lt;/span&gt; &lt;span class="n"&gt;N&lt;/span&gt; &lt;span class="o"&gt;+&lt;/span&gt; &lt;span class="n"&gt;k&lt;/span&gt;&lt;span class="p"&gt;]&lt;/span&gt; &lt;span class="o"&gt;*&lt;/span&gt; &lt;span class="n"&gt;B&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="n"&gt;k&lt;/span&gt; &lt;span class="o"&gt;*&lt;/span&gt; &lt;span class="n"&gt;N&lt;/span&gt; &lt;span class="o"&gt;+&lt;/span&gt; &lt;span class="n"&gt;col&lt;/span&gt;&lt;span class="p"&gt;];&lt;/span&gt;&#10;&lt;/span&gt;&lt;/span&gt;&lt;span class="line"&gt;&lt;span class="cl"&gt; &lt;span class="p"&gt;}&lt;/span&gt;&#10;&lt;/span&gt;&lt;/span&gt;&lt;span class="line"&gt;&lt;span class="cl"&gt; &lt;span class="n"&gt;C&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="n"&gt;row&lt;/span&gt; &lt;span class="o"&gt;*&lt;/span&gt; &lt;span class="n"&gt;N&lt;/span&gt; &lt;span class="o"&gt;+&lt;/span&gt; &lt;span class="n"&gt;col&lt;/span&gt;&lt;span class="p"&gt;]&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;sum&lt;/span&gt;&lt;span class="p"&gt;;&lt;/span&gt;&#10;&lt;/span&gt;&lt;/span&gt;&lt;span class="line"&gt;&lt;span class="cl"&gt; &lt;span class="p"&gt;}&lt;/span&gt;&#10;&lt;/span&gt;&lt;/span&gt;&lt;span class="line"&gt;&lt;span class="cl"&gt;&#10;&lt;/span&gt;&lt;/span&gt;&lt;span class="line"&gt;&lt;span class="cl"&gt;&lt;span class="p"&gt;}&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&#10;&lt;/div&gt;&#10;&lt;p&gt;A prima vista, sembra facile. Basta calcolare ogni cella della matrice di output C. Questo codice kernel sembra funzionare. Ecco una rappresentazione più visiva del kernel.&lt;/p&gt;</description></item></channel></rss>