SiFive’s Post

When moving CUDA code to SYCL, the Intel DPC++ Compatibility Tool provides predefined rules, but they may not always optimize your code effectively. Learn how to use user-defined migration rules better to translate CUDA constructs to SYCL for optimal results. #IAmIntel https://bit.ly/3TidJUi

When moving CUDA code to SYCL, the Intel DPC++ Compatibility Tool provides predefined rules, but they may not always optimize your code effectively. Learn how to use user-defined migration rules better to translate CUDA constructs to SYCL for optimal results. #IAmIntel https://bit.ly/47llsXD

When moving CUDA code to SYCL, the Intel DPC++ Compatibility Tool provides predefined rules, but they may not always optimize your code effectively. Learn how to use user-defined migration rules better to translate CUDA constructs to SYCL for optimal results. #IAmIntel https://bit.ly/3Xzbgr6

When transitioning your existing CUDA code to #SYCL, the Intel DPC++ Compatibility Tool offers a range of predetermined rules that assist with the migration process. However, these default rules may not deliver the most efficient optimization for your particular situation. Learn how to use user-defined migration rules to specify how specific CUDA constructs should be translated to SYCL to achieve the best possible results: https://intel.ly/4cwuy5m #oneAPI

 Thread-Safe Counter AtomicInteger counter = new AtomicInteger(); IntStream.range(0, 10).parallel().forEach(i -> counter.incrementAndGet()); System.out.println(counter.get()); // Output: 10 How it works: AtomicInteger ensures thread-safe increments. parallel() runs tasks in parallel. #Multithreading #JavaConcurrency #ProgrammingTips

The fully SYCL2020 conformant Intel #oneAPI DPC++/C++ introduced the kernel_compiler extension that takes OpenCL C and SPIR-V code stubs and compiles them at runtime to #SYCL kernels for execution on a device. Read more: https://intel.ly/4aEo0zY

Vectorizing code for SIMD acceleration is easy and fun. For a CPU with massive bandwidth, like #IBMPower with OMI-DDR5, it's essential to get massive performance. We walk through getting the compiler to vectorize a loop, Altivec intrinsics, and, finally, how to write your code in a platform-independent way. https://lnkd.in/g45JbZWP

Massive upgrade in our new ASIC (Astro, Svelte, Internet Computer/Identity) stack: You can now effortlessly upload, change, or delete your profile image with simple drag-and-drop functionality! Check out the live demo: https://lnkd.in/d4HP6mPW #IcAcademy #InternetComputer #Svelte 

There is a growing demand on bare metal performance for LLMs which can be made to run on low end devices. Particularly from compiler benchmarking, applications such as llama.cpp ,whisper.cpp and many others are extremely important. With great collaboration from Georgi Gerganov and Intel Corporation, we have enabled SYCL runtime for llama.cpp and whisper.cpp. This is one of the major applications which use #oneapi and #SYCL runtime (Intel® oneAPI DPC++ Library) for efficient performance across different vendor compiler runtimes. Intel Software #compiler #sycl #llms #oneapi #genai #clang

Introducing Open-Binius! 🚀 "Hardware IPs for accelerating ZK proofs over binary fields" Open-source (MIT) FPGA code. A community effort with academia. Check out our initial results in the table below. LFG! 🔗Github Repo: in first comment 👇 #opensource #zkp