Lecture 2: Vulkan, Part 1 COMP5822M – High Perf. Graphics – Practicalstuff – WhyVulkan? – VulkanBasics Copyright By PowCoder代写 加微信 powcoder COMP5822M – High Perf. Graphics Practical, I – Labsupportsessions,suggestion – Tuesday afternoon, online, 15.00 to
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Overview GPU atomics Atomic (compare and) exchange Summary and next lecture XJCO3221 Parallel Computation University of Leeds Copyright By PowCoder代写 加微信 powcoder Lecture 18: Atomic operations XJCO3221 Parallel Computation GPU atomics Previous lectures Atomic (compare and) exchange Today¡¯s lecture Summary and next lecture Previous lectures Whenever multiple processing units had read-write access to the same
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18-646 – How to Write Fast Code? 1 Carnegie Mellon University Course Information Lectures: Tuesday and Thursday 6:00pm-7:20pm ET Office Hours: Instructor Office Hours: Wednesdays 4:30pm-5:30pm ET TA Office Hours: TBD Course Links: Canvas: https://canvas.cmu.edu/courses/21510/pages/course-schedule Piazza: https://piazza.com/class/kkmp02yc92h598 Gradescope: https://www.gradescope.com/courses/241050 18-6456 – How to Write Fast Code?
18-646 – How to Write Fast Code II 1 Carnegie Mellon University How to Write Fast Code? Fast Platforms Multicore platforms Manycore platforms Cloud platforms Good Techniques Data structures Algorithms Software Architecture Course Goals To write fast code for your research/application, you should: 1. Feel comfortable hacking
In [0]: from tensorflow.python.client import device_lib print(“Show System RAM Memory:\n\n”) !cat /proc/meminfo | egrep “MemTotal*” print(“\n\nShow Devices:\n\n”+str(device_lib.list_local_devices())) Show System RAM Memory: MemTotal: 13335188 kB Show Devices: [name: “/device:CPU:0” device_type: “CPU” memory_limit: 268435456 locality { } incarnation: 8700158686858789265 , name: “/device:XLA_CPU:0” device_type: “XLA_CPU” memory_limit: 17179869184 locality { } incarnation: 8342104309289264332 physical_device_desc: “device: XLA_CPU device” , name: “/device:XLA_GPU:0”
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In [0]: from tensorflow.python.client import device_lib print(“Show System RAM Memory:\n\n”) !cat /proc/meminfo | egrep “MemTotal*” print(“\n\nShow Devices:\n\n”+str(device_lib.list_local_devices())) Show System RAM Memory: MemTotal: 13335188 kB Show Devices: [name: “/device:CPU:0” device_type: “CPU” memory_limit: 268435456 locality { } incarnation: 8700158686858789265 , name: “/device:XLA_CPU:0” device_type: “XLA_CPU” memory_limit: 17179869184 locality { } incarnation: 8342104309289264332 physical_device_desc: “device: XLA_CPU device” , name: “/device:XLA_GPU:0”
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Laboratory #2 Tensorflow and CNN Table of Contents Step1. GPU ……………………………………………………………………………………………………………………. 1 Step2. Implement handwritten recognition in Tensorflow using CNN …………………………………… 7 Step3. Text mining using CNN…………………………………………………………………………………………. 9 3.1. Pre-processing:……………………………………………………………………………………………………… 9 3.2. Embedded word: …………………………………………………………………………………………………. 12 3.3. Model training:……………………………………………………………………………………………………. 13 One of the main reasons in recent year’s breakthrough of DNN is the power of
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In [0]: from tensorflow.python.client import device_lib print(“Show System RAM Memory:\n\n”) !cat /proc/meminfo | egrep “MemTotal*” print(“\n\nShow Devices:\n\n”+str(device_lib.list_local_devices())) Show System RAM Memory: MemTotal: 13335188 kB Show Devices: [name: “/device:CPU:0” device_type: “CPU” memory_limit: 268435456 locality { } incarnation: 8700158686858789265 , name: “/device:XLA_CPU:0” device_type: “XLA_CPU” memory_limit: 17179869184 locality { } incarnation: 8342104309289264332 physical_device_desc: “device: XLA_CPU device” , name: “/device:XLA_GPU:0”
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18-646 – How to Write Fast Code II? 1 Carnegie Mellon University Ian Lane 18646 – Week 2 18-646 – How to Write Fast Code II? 2 Homework 1 18-646 – How to Write Fast Code II? 3 Lecture Questions https://canvas.cmu.edu/courses/21510/quizzes/55580 18-646 – How to Write Fast Code II? 4 Outline – Follow on from
School of Computing and Information Systems The University of Melbourne COMP90042 NATURAL LANGUAGE PROCESSING (Semester 1, 2021) Workshop exercises: Week 7 1. What are contextual representations? Discussion 2. How does a transformer captures dependencies between words? What advan- tages does it have compared to RNN? 3. What is discourse segmentation? What do the segments consist