PPT-GPU Threads and Scheduling

Instructor Notes This lecture deals with how work groups are scheduled for execution on the compute units of devices Also explain the effects of divergence of work items within a group and its negative effect on performance. Dr A . Sahu. Dept of Comp Sc & . Engg. . . IIT . Guwahati. 1. Outline. Graphics System . GPU Architecture. Memory Model. Vertex Buffer, Texture buffer. GPU Programming Model. DirectX. , OpenGL, . Main-Memory Workloads. Iraklis. . Psaroudakis. (EPFL). , Tobias Scheuer (SAP AG), Norman May (SAP AG), Anastasia Ailamaki (EPFL). 1. Scheduling for high concurrency. 2. Queries >> H/W contexts. CS . 355. Operating Systems. Dr. Matthew Wright. Operating System Concepts. chapter 5. Basic Concepts. Process execution consists of a . cycle. of CPU execution and I/O . wait.. Typical CPU Burst Distribution. Jim Fawcett. CSE687 – Object Oriented Design. Spring 2016. Windows API. Create, style, and manage windows. Manage Files and Directories. Create and manage processes, threads, and synchronizers. Load and Unload Dynamic Link Libraries. Kicking and Screaming:. Multicore Architecture and Video Games. Summary of Topics:. Console Architecture. Meaning of Paper’s Title/Why the Video Game Developer HATED the new. Techniques/Problems. The Future. Lecture 2: more basics. Recap. Can use GPU to solve highly parallelizable problems. Straightforward extension to C++. Separate CUDA code into .cu and .. cuh. files and compile with . nvcc. to create object files (.o files). Achim Lösch. and Marco . Platzner. {. achim.loesch. , . platzner. }@upb.de. Heterogeneous Compute Node. Contribution:. Novel energy-optimizing list scheduling approach for single heterogeneous compute nodes based on real measurements. Multithreaded Processors. Michael C Shebanow. Sr. Architecture Manager, GPUs. Agenda. GPU Computing. Tesla Products. CUDA. SM: Thread Multiprocessor. Application Performance Model. GPU Computing. GPU = graphics processing unit. 1. Chapter 10. Multiprocessor and. Real-Time Scheduling. BYU CS 345. Chapter 10 - Multiprocessor and Read-Time Scheduling. 2. Classifications of Multiprocessors. Loosely coupled multiprocessor.. each processor has its own memory and I/O channels. for High-End CPU-GPU Architectures. Vignesh. Ravi. Dept. of Computer Science and Engineering. Advisor: . Gagan. . Agrawal. 1. The Death of Single-core CPU Scaling. 2. The Landscape of Computing – Moore’s Law. Single machine, multi-core. P(OSIX) threads: bare metal multi-threading. OpenMP. :. compiler directives that implement various constructs like parallel-for. Single machine, GPU. CUDA/. OpenCL. :. bare metal GPU coding. CS 179: GPU Programming Lecture 7 Week 3 Goals: Advanced GPU- accelerable algorithms CUDA libraries and tools This Lecture GPU- accelerable algorithms: Reduction Prefix sum Stream compaction Sorting (quicksort) Scheduling Techniques for GPU Architectures with Processing-In-Memory Capabilities Ashutosh Pattnaik Xulong Tang, Adwait Jog, Onur Kay ı ran, Asit Mishra, Mahmut Kandemir , Onur Mutlu Martin Burtscher. Department of Computer Science. High-end CPU-GPU Comparison. . Xeon 8180M. . Titan V. Cores 28 5120 (+ 640). Active threads 2 per core 32 per core. Frequency 2.5 (3.8) GHz 1.2 (1.45) GHz.