rights reserved. Presenter: Haichen Shen, Yao Wang Amazon SageMaker Neo, Deep Engine Science Dynamic Model in TVM AWS AI© 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Models with with dynamism ● Control flow (if, loop, etc) ● Dynamic shapes ○ Dynamic inputs: batch size, image size, sequence length, etc. ○ Output shape of some ops are data dependent: arange, nms, etc. ○ Control models© 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Support dynamic model in TVM ● Support Any-dim in typing ● Use shape function to compute the type at runtime ● Virtual

std::ranges::transform_view(range, add_two); Ranges LibraryRanges Library - Have begin() and end() - Often have size() - Random access: access any element at random in constant time - Contiguous: a contiguous block algorithm(1.0f, 3, data); // Send data to proc. 1 MPI_Send(values.data(), values.size(), MPI_FLOAT, 1, 0, MPI_COMM_WORLD); // Data is now sent. Process 0 Process 1 algorithm(1.0f, 3, data); // Send data to proc. 1 MPI_Send(values.data(), values.size(), MPI_FLOAT, 1, 0, MPI_COMM_WORLD); // Data is now sent. // Allocate space for

Memory Model C++11 – C++23About Me: alex.dathskovsky@speedata.io www.linkedin.com/in/alexdathskovsky https://www.cppnext.comAlex Dathskovsky | alex.dathskovsky@speedata.io | www.linkedin.com/in/a

comAn Introduction struct region { int size; }; void init_region(char* backing_buffer, size_t buffer_size ) { if(buffer_size < current_region.size) { LOG(“Buffer size too small”); return; } //other init }An Introduction struct region { int size; }; void init_region(char* backing_buffer, size_t buffer_size ) { if(buffer_size < current_region.size) { LOG(“Buffer size too small”); return; } //other init Introduction struct region { int size; }; void init_region(char* backing_buffer, size_t buffer_size ) { if(buffer_size < (size_t)current_region.size) { LOG(“Buffer size too small”); return; } //other

Significant adoption in Embedded, Desktop and HPC markets Improved programmability, smaller code size, faster performance Based on C++17, backwards compatible with SYCL 1.2.1 Simplify porting of standard sycl::buffer bufA{dA.data(), sycl::range{dA.size()}}; sycl::buffer bufB{dB.data(), sycl::range{dB.size()}}; sycl::buffer bufO{dO.data(), sycl::range{dO.size()}}; gpuQueue.submit([&](sycl::handler sycl::read_only); sycl::accessor out(bufO, cgh, sycl::write_only); cgh.parallel_for(sycl::range{dA.size()}, [=](id<1> i){ out[i] = inA[i] + inB[i]; }); }); gpuQueue.wait_and_throw();

that I can modify in the Inspector pane. I put a check mark in the Best Fit box and changed the Max Size to be 100. Now, I’ll run the project. Zenva Academy – Online courses on game, web and mobile We will make the Canvas component display at the same resolution as the Camera and set screen size parameters. Zenva Academy – Online courses on game, web and mobile app development ©2016 Zenva Page 33 The Content Panel should have the Content from the Viewport in it. Now, change the size of the Data to be 7. Zenva Academy – Online courses on game, web and mobile app development

variant::variant(T&& t) : index{find_index} // Powered by TMP , // ... { } template<size_t I, class... Ts> constexpr auto get(tuple&&) noexcept -> typename tuple_element::variant(T&& t) : index{find_index} // Powered by TMP , // ... { } template<size_t I, class... Ts> constexpr auto get(tuple&&) noexcept -> typename tuple_element::variant(T&& t) : index{find_index} // Powered by TMP , // ... { } template<size_t I, class... Ts> constexpr auto get(tuple&&) noexcept -> typename tuple_element

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writing styles 59 For few-shot prompting with classification tasks, mix up the classes 59 Adapt to model updates 60 Experiment with output formats 60 JSON Repair 61 Working with Schemas 62 Experiment When thinking about a large language model input and output, a text prompt (sometimes accompanied by other modalities such as image prompts) is the input the model uses to predict a specific output. You can be complicated. Many aspects of your prompt affect its efficacy: the model you use, the model’s training data, the model configurations, your word-choice, style and tone, structure, and context

Change Happening Faster Than Ever? Yes, It Is • AI User + Usage + CapEx Growth = Unprecedented • AI Model Compute Costs High / Rising + Inference Costs Per Token Falling = Performance Converging + Developer 2/24 2/25 4/25 75% 60% 10% 21% 15% 0% Details on Page 293 USA – LLM #1 China USA – LLM #2 AI Model Compute Costs High / Rising + Inference Costs Per Token Falling = Performance Converging + Developer Change Happening Faster Than Ever? Yes, It Is • AI User + Usage + CapEx Growth = Unprecedented • AI Model Compute Costs High / Rising + Inference Costs Per Token Falling = Performance Converging + Developer

PGAS Model, RDMA Building Remote Pointer Types Building Distributed Data Structures Extending to GPUsThis Talk Background: how do we write a program for a supercomputer? Introduce PGAS Model, RDMA PGAS Model, RDMA Building Remote Pointer Types Building Distributed Data Structures Extending to GPUsThis Talk Background: how do we write a program for a supercomputer? Introduce PGAS Model, RDMA Extending to GPUsThis Talk Background: how do we write a program for a supercomputer? Introduce PGAS Model, RDMA Building Remote Pointer Types Building Distributed Data Structures Extending to GPUsWhat