Create joint-matrix-a-unified-sycl-extension-for-matrix-hardware-programming.md

Author: codeplaymax

content/videos/2025/joint-matrix-a-unified-sycl-extension-for-matrix-hardware-programming.md

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+---
+contributor: max
+date: '2025-05-07T12:11:00'
+title: 'Joint Matrix: A Unified SYCL Extension for Matrix Hardware Programming'
+external_url: 'https://www.youtube.com/watch?v=BBESHzVVDYo'
+type: presentation
+tags:
+  - sycl
+  - oneapi
+---
+
+Joint Matrix: A Unified SYCL Extension for Matrix Hardware Programming
+
+Joint matrix is a new SYCL extension for matrix hardware programming. It unifies 
+targets like Intel Advanced Matrix Extensions (Intel AMX), Intel Xe Matrix Extensions
+(Intel XMX), NVIDIA* Tensor Cores, AMD* Matrix Cores, etc. In general, ML frameworks
+like Tensorflow and libraries like oneAPI Deep Neural Network Library (oneDNN) are capable
+of heavily utilizing matrix hardware acceleration, and are the answer for many types
+of users and applications who want high performance from such hardware. However,
+for users who want to build their own neural network applications, these libraries
+and frameworks become too high-level, because users cannot do custom optimizations,
+and too heavyweight, because the size of libraries is large. Moreover, new operations
+are often introduced in the machine learning domain for which frameworks and libraries
+do not provide timely and performant solutions. For such cases, APIs are needed to
+write custom workload-specific optimizations and this is where joint matrix can help.
+Joint matrix has a lower level of abstraction than these frameworks and libraries,
+enabling it to provide performance, productivity, and fusion capabilities but, at the
+same time, offers portability by using one code to target different matrix hardware.
+
+In this talk, we present (1) the main APIs introduced as part of SYCL joint matrix extension,
+(2) tuning techniques to fully utilize Intel hardware using SYCL joint matrix, and (3)
+the application and validation of this language feature and tuning techniques using the
+GEMM benchmark and the ability to fuse kernels such as GEMM and GELU.