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Kernels are functions that run on GPUs in parallel across multiple threads and blocks. Kernels are launched with an execution configuration that defines the number of blocks in the grid and the number of threads in each block, allowing thousands of threads to run simultaneously and take advantage of the GPU's parallel architecture.
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Ac cuda c_5
The document discusses the issue that can occur when there are more threads launched than elements of data to operate on. If a thread's calculated data index is greater than the number of elements, it will attempt to access non-existent data and result in a runtime error. The code must check that each thread's data index is less than the total number of elements before performing work.
Jdk 7 4-forkjoin
This document discusses Fork/Join framework in Java 7. It explains that Fork/Join is designed to maximize usage of multiple processors by recursively splitting large tasks into smaller subtasks. It uses work-stealing algorithm where idle workers can steal tasks from busy workers' queues to balance load. An example of calculating Fibonacci numbers using Fork/Join is provided where the task is split recursively until the subproblem size is smaller than threshold, at which point it is computed directly.
Accelerating microbiome research with OpenACC
Presented at OpenACC Summit 2020.
UniFrac is a commonly used metric in microbiome research for comparing microbiome profiles to one another. Computing UniFrac on modest sample sizes used to take a workday on a server class CPU-only node, while modern datasets would require a large compute cluster to be feasible. After porting to GPUs using OpenACC, the compute of the same modest sample size now takes only a few minutes on a single NVIDIA V100 GPU, while modern datasets can be processed on a single GPU in hours. The OpenACC programming model made the porting of the code to GPUs extremely simple; the first prototype was completed in just over a day. Getting full performance did however take much longer, since proper memory access is fundamental for this application.
こわくないよ❤️ Playframeworkソースコードリーディング入門
This document discusses the Play framework and how it starts a Play application from the command line using the ProdServerStart object.
1. The ProdServerStart object's main method creates a RealServerProcess and calls the start method, passing the process.
2. The start method reads server configuration settings, creates a PID file, starts the Play application, and starts the HTTP server provider.
3. It loads the Play application using an ApplicationLoader, which can use Guice for dependency injection. The loaded application is then started by Play.
John Melesky - Federating Queries Using Postgres FDW @ Postgres Open
This document discusses federating queries across PostgreSQL databases using foreign data wrappers (FDWs). It begins by introducing the author and their background. It then covers using FDWs to partition tables across multiple nodes for queries, the benefits over traditional views, and demonstrates counting rows across nodes. It notes limitations like network overhead, lack of keys/constraints, and single-threaded execution. Finally, it discusses strategies like using many small nodes, node-level partitioning, distributed processing, and multi-headed setups to optimize federated querying.
kubernetes practice
The document provides instructions for setting up a Kubernetes cluster with one master node and one worker node on VirtualBox. It outlines the system requirements for the nodes, describes how to configure the networking and hostnames, install Docker and Kubernetes, initialize the master node with kubeadm init, join the worker node with kubeadm join, and deploy a test pod. It also includes commands to check the cluster status and remove existing Docker installations.
Java gpu computing
De gemiddelde GPU bevat tegenwoordig meer PK's dan de CPU. Naar aanleiding hiervan komen er steeds meer mogelijkheden om computationele problemen te verplaatsen van de CPU naar de GPU. Deze presentatie zal een inleiding zijn hoe je dit in Java kunt doen met behulp van Jogamp JoCL. Aan de hand van enkele simpele problemen wordt aangetoond wanneer een GPU beter ingezet kan worden dan een CPU en vice versa. Dit is ook een van de speerpunten in Java 9 (Project Sumatra) wat o.a. JoCL als inspiratie gebruikt.
Nvvp streams-3
cudaMemcpyAsync allows asynchronous transfer of memory between the CPU and GPU over a non-default stream, enabling overlap of memory copies and computation so that copies do not block the CPU or GPU. This can improve performance by hiding memory transfer latency with useful work.
Introduction to Accelerators
This document provides an introduction to accelerators such as GPUs and Intel Xeon Phi. It discusses the architecture and programming of GPUs using CUDA. GPUs are massively parallel many-core processors designed for graphics processing but now used for general purpose computing. They provide much higher floating point performance than CPUs. The document outlines GPU memory architecture and programming using CUDA. It also provides an overview of Intel Xeon Phi which contains over 50 simple CPU cores for highly parallel workloads.
Spark performance tuning eng
This document summarizes a performance test of data transfers using Spark. It tested transferring 15 million and 100 million records between Oracle databases and HDFS using different Spark configurations. For 15 million records, using a single executor with 7 cores took 6.7 minutes for Oracle to Oracle and 5.7 minutes for Oracle to HDFS. For 100 million records, the times were 47 minutes and 51 minutes respectively. The conclusion is that generating more executors from the same resources can improve performance, and setting the number of partitions is important for data manipulation.
NodeJSnodesforfreeinmyworldgipsnndnnd.pdf
This document contains lecture notes on Node.js. It discusses how Node.js uses an event-driven, non-blocking I/O model with callbacks instead of threads. It implements this using an event loop that processes callbacks from an event queue. Standard modules like the file system module wrap synchronous OS calls to utilize asynchronous callbacks. Node.js also includes a Buffer class for efficient binary data handling and a module system for reusable code packages.
Eco-friendly Linux kernel development
Eco-friendly Linux kernel development focuses on minimizing energy consumption during continuous integration and delivery (CI/CD). The presenter from Canonical discusses how building minimal kernels with KernelCraft and deploying them in virtual machines can significantly reduce the time and power required for kernel testing compared to building full custom kernels. Experimental results show KernelCraft can cut kernel build time by over 80% and energy usage by over 90% for the same test. While challenges remain around coverage and service testing, efforts like virtualizing the build environment and migrating to more efficient tools can improve the sustainability of kernel development at large scale.
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Ac cuda c_2
- 3. performWork<<<2, 4>>>() GPUs do work in parallel GPU
- 4. performWork<<<2, 4>>>() GPU work is done in a thread GPU
- 5. performWork<<<2, 4>>>() Many threads run in parallel GPU
- 6. A collection of threads is a block GPU performWork<<<2, 4>>>()
- 7. There are many blocks GPU performWork<<<2, 4>>>()
- 8. A collection of blocks is a grid GPU performWork<<<2, 4>>>()
- 9. GPU functions are called kernels GPU performWork<<<2, 4>>>()
- 10. Kernels are launched with an execution configuration GPU performWork<<<2, 4>>>()
- 11. performWork<<<2, 4>>>() The execution configuration defines the number of blocks in the grid GPU
- 12. performWork<<<2, 4>>>() … as well as the number of threads in each block GPU
- 13. performWork<<<2, 4>>>() GPU Every block in the grid contains the same number of threads