This document describes OpenCL buffer management and provides examples of its use. It discusses declaring buffers, copying data between the host and device, and provides simple examples of image rotation and matrix multiplication. The goal is to demonstrate the basic OpenCL host code needed for buffer handling and to serve as templates for more complex kernels.
This document discusses optimizations for implementing an N-body simulation algorithm on GPUs using OpenCL. It begins with an overview of the basic N-body algorithm and its parallel implementation. Two key optimizations are explored: using local memory to enable data reuse across work items, and unrolling the computation loop. Performance results on AMD and Nvidia GPUs show that data reuse provides significant speedup, and loop unrolling further improves performance on the AMD GPU. An example N-body application is provided to experiment with these optimization techniques.
This document discusses synchronization, timing, and profiling in OpenCL. It covers coarse-grained synchronization at the command queue level and fine-grained synchronization at the function call level using events. It describes how to use events for timing, profiling, and asynchronous host-device communication. It provides an example of how asynchronous I/O can improve performance in medical imaging applications by overlapping computation and data transfers.
This document discusses approaches to programming multiple devices in OpenCL, including using a single context with multiple devices or multiple contexts. With a single context, memory objects are shared but data must be explicitly transferred between devices. Multiple contexts allow splitting work by device but require extra communication. Load balancing work between heterogeneous CPUs and GPUs requires considering scheduling overhead and data location.
OpenCL allows parallel computing on heterogeneous devices like CPUs, GPUs, and other processors. It defines a platform model consisting of a host connected to OpenCL devices divided into compute units and processing elements. The lecture introduces OpenCL concepts like contexts, command queues, memory objects, and programs. It demonstrates creating and using these objects to set up and run a simple vector addition program on an OpenCL device.
This document discusses how work groups are scheduled for execution on GPU compute units. It explains that work groups are broken down into hardware schedulable units known as warps or wavefronts. These group threads together and execute instructions in lockstep. The document covers thread scheduling, effects of divergent control flow, predication, warp voting, and optimization techniques like maximizing occupancy.
This document discusses three important optimizations for GPU performance: thread mapping, device occupancy, and vectorization. Thread mapping involves assigning threads to data in a way that aligns with hardware and provides efficient memory access. Device occupancy refers to how fully the compute unit resources are utilized. Having enough active threads to hide memory latency impacts performance. Vectorization, or processing multiple data elements with each thread, is particularly important for AMD GPUs. Examples are provided of different thread mappings and how they affect memory access and performance.
Accelerating Habanero-Java Program with OpenCL GenerationAkihiro Hayashi
Accelerating Habanero-Java Program with OpenCL Generation. Akihiro Hayashi, Max Grossman, Jisheng Zhao, Jun Shirako, Vivek Sarkar. 10th International Conference on the Principles and Practice of Programming in Java (PPPJ), September 2013.
This document discusses optimizations for implementing an N-body simulation algorithm on GPUs using OpenCL. It begins with an overview of the basic N-body algorithm and its parallel implementation. Two key optimizations are explored: using local memory to enable data reuse across work items, and unrolling the computation loop. Performance results on AMD and Nvidia GPUs show that data reuse provides significant speedup, and loop unrolling further improves performance on the AMD GPU. An example N-body application is provided to experiment with these optimization techniques.
This document discusses synchronization, timing, and profiling in OpenCL. It covers coarse-grained synchronization at the command queue level and fine-grained synchronization at the function call level using events. It describes how to use events for timing, profiling, and asynchronous host-device communication. It provides an example of how asynchronous I/O can improve performance in medical imaging applications by overlapping computation and data transfers.
This document discusses approaches to programming multiple devices in OpenCL, including using a single context with multiple devices or multiple contexts. With a single context, memory objects are shared but data must be explicitly transferred between devices. Multiple contexts allow splitting work by device but require extra communication. Load balancing work between heterogeneous CPUs and GPUs requires considering scheduling overhead and data location.
OpenCL allows parallel computing on heterogeneous devices like CPUs, GPUs, and other processors. It defines a platform model consisting of a host connected to OpenCL devices divided into compute units and processing elements. The lecture introduces OpenCL concepts like contexts, command queues, memory objects, and programs. It demonstrates creating and using these objects to set up and run a simple vector addition program on an OpenCL device.
This document discusses how work groups are scheduled for execution on GPU compute units. It explains that work groups are broken down into hardware schedulable units known as warps or wavefronts. These group threads together and execute instructions in lockstep. The document covers thread scheduling, effects of divergent control flow, predication, warp voting, and optimization techniques like maximizing occupancy.
This document discusses three important optimizations for GPU performance: thread mapping, device occupancy, and vectorization. Thread mapping involves assigning threads to data in a way that aligns with hardware and provides efficient memory access. Device occupancy refers to how fully the compute unit resources are utilized. Having enough active threads to hide memory latency impacts performance. Vectorization, or processing multiple data elements with each thread, is particularly important for AMD GPUs. Examples are provided of different thread mappings and how they affect memory access and performance.
Accelerating Habanero-Java Program with OpenCL GenerationAkihiro Hayashi
Accelerating Habanero-Java Program with OpenCL Generation. Akihiro Hayashi, Max Grossman, Jisheng Zhao, Jun Shirako, Vivek Sarkar. 10th International Conference on the Principles and Practice of Programming in Java (PPPJ), September 2013.
This is the final report for my project as a Technical Student at CERN.
The Intel Xeon/Phi platform is a powerful x86 multi-core engine with a very high-speed memory interface. In its next version it will be able to operate as a stand-alone system with a very high-speed interconnect. This makes it a very interesting candidate for (near) real-time applications such as event-building, event-sorting and event preparation for subsequent processing by high level trigger software algorithms.
This document discusses strategies for task and data parallelism in .NET. It begins with an overview of tasks and how they provide a cheaper alternative to threads for parallelizing work. Various APIs for parallelism are covered, including Parallel Loops, PLINQ, and task continuations. Best practices are provided around uneven work distribution, dependency management, minimizing synchronization, and leveraging lock-free and dataflow patterns. The document concludes with tips on profiling, SIMD, and GPU parallelism.
Adam Sitnik "State of the .NET Performance"Yulia Tsisyk
MSK DOT NET #5
2016-12-07
In this talk Adam will describe how latest changes in.NET are affecting performance.
Adam wants to go through:
C# 7: ref locals and ref returns, ValueTuples.
.NET Core: Spans, Buffers, ValueTasks
And how all of these things help build zero-copy streams aka Channels/Pipelines which are going to be a game changer in the next year.
OpenGL 4.4 provides new features for accelerating scenes with many objects, which are typically found in professional visualization markets. This talk will provide details on the usage of the features and their effect on real-life models. Furthermore we will showcase how more work for rendering a scene can be off-loaded to the GPU, such as efficient occlusion culling or matrix calculations.
Video presentation here: http://on-demand.gputechconf.com/gtc/2014/video/S4379-opengl-44-scene-rendering-techniques.mp4
The document discusses the process for context switching between tasks in Rust. It explains that the current task is grabbed from thread-local storage and its ability to sleep is checked. The next task and cleanup function are prepared. Unsafe transmutes are used to get mutable references to tasks. The task contexts are swapped using a raw operation, placing the scheduler and next task in the proper locations. On the return swap, the cleanup function is immediately run.
Putting a Fork in Fork (Linux Process and Memory Management)David Evans
The document discusses several topics related to computer science class cs4414 at University of Virginia:
- Updates were due Sunday at 11:59pm including progress updates and scheduling design reviews.
- Tuesday's class will feature a guest lecture on authentication using single sign-on.
- The last class covered translation lookaside buffers and paging/segmentation concepts.
- A code sample is shown and analyzed that causes a segmentation fault due to accessing memory outside the allocated space.
- Details are provided on limiting resources and viewing process limits.
Solving Endgames in Large Imperfect-Information Games such as PokerKarel Ha
My master thesis on solving endgames in imperfect-information games.
keywords: algorithmic game theory, imperfect-information games, Nash equilibrium, subgame, endgame, counterfactual regret minimization, Poker
The document discusses distributed programming using Java sockets and RMI. It describes UDP and TCP protocols, with UDP being connectionless and less reliable while TCP is connection-oriented. It then covers socket programming using DatagramSocket and DatagramPacket for UDP, and Socket and input/output streams for TCP. The document also discusses implementing a remote interface using RMI to allow method calls between JVMs.
This document summarizes a research paper that proposes a parallel k-nearest neighbors (kNN) algorithm using OpenCL on a GPU architecture. The key points are:
1) kNN classification is computationally intensive, especially for large datasets, creating a need for parallelization.
2) The authors designed and implemented a parallel kNN algorithm using OpenCL to distribute distance computations across GPU cores.
3) Experimental results on UCI datasets showed the parallel kNN approach improved performance over a serial kNN implementation.
Kenneth presented a summary of his participation in the Kaggle TGS Salt Identification Challenge. He discussed the top solutions which utilized techniques like ResNeXt and ResNet backbones, feature pyramid attention, Lovasz loss, and test time augmentation. Kenneth's own solution ranked 1482nd, using a U-Net architecture with Lovasz loss and test time flipping. He analyzed techniques that did and did not work well for this salt segmentation task from radar images.
The document discusses the Rust runtime and how it interfaces with the Linux kernel to spawn new processes. It covers how fork() is implemented at the libc level and how it ultimately triggers a system call by generating an interrupt. It also discusses entering supervisor mode in the kernel and recaps some PS3 benchmarking results.
Efficient Dynamic Scheduling Algorithm for Real-Time MultiCore Systems iosrjce
Imprecise computation model is used in dynamic scheduling algorithm having heuristic function to
schedule task sets. A task is characterized by ready time, worst case computation time, deadline and resource
requirements. A task failing to meet its deadline and resource requirements on time is split into mandatory part
and optional part. These sub-tasks of a task can execute concurrently on multiple cores, thus achieving
parallelization provided by the multi-core system. Mandatory part produces acceptable results while optional
part refines the result further. To study the effectiveness of proposed scheduling algorithm, extensive simulation
studies have been carried out. Performance of proposed scheduling algorithm is compared with myopic and
improved myopic scheduling algorithm. The simulation studies shows that schedulability of task split myopic
algorithm is always higher than myopic and improved myopic algorithm.
The document outlines the schedule and content for a university computer science class. It includes an introduction to scheduling, a kernel timer winner, scheduling demos from student teams, a discussion of scheduling strategies like first-come first-served and round-robin, and a review of priority scheduling. It also announces details about an upcoming midterm exam and shows the results of a Rust program performance test on different laptops.
This document discusses Q-normalization, a method for normalizing gene expression data. It presents parallel implementations of Q-normalization using shared memory, message passing, and GPU architectures. Benchmarking shows the GPU implementation provides a 5.5x speedup over the sequential CPU version for processing large gene expression datasets. The shared memory implementation provides a 2.9x total speedup, while the message passing version is suitable for distributed memory clusters.
Compressed Sensing using Generative Modelkenluck2001
This document summarizes Kenneth Emeka Odoh's presentation on using generative models for compressed sensing. It introduces compressed sensing and describes how generative models like variational autoencoders (VAEs) and generative adversarial networks (GANs) can be used to solve the underdetermined linear systems that arise in compressed sensing. The document also compares VAEs to standard autoencoders and discusses how generative models may require fewer training examples due to regularization imposing structure on the problem.
This document summarizes lecture slides from a university computer science class on operating system processes and virtual memory. The slides covered:
- Last week's discussion of process creation in Rust and the fork system call.
- The plan for this week's lectures, including how the kernel makes processes and diving into the fork.c source code.
- An overview of virtual memory and how it provides memory isolation between processes using paging and segmentation.
- Details of how x86 processors implement virtual memory using segmentation and paging tables, address translation, and handling of page faults.
- The history and evolution of virtual memory from early mainframe systems to modern desktop processors.
Building High-Performance Language Implementations With Low EffortStefan Marr
This talk shows how languages can be implemented as self-optimizing interpreters, and how Truffle or RPython go about to just-in-time compile these interpreters to efficient native code.
Programming languages are never perfect, so people start building domain-specific languages to be able to solve their problems more easily. However, custom languages are often slow, or take enormous amounts of effort to be made fast by building custom compilers or virtual machines.
With the notion of self-optimizing interpreters, researchers proposed a way to implement languages easily and generate a JIT compiler from a simple interpreter. We explore the idea and experiment with it on top of RPython (of PyPy fame) with its meta-tracing JIT compiler, as well as Truffle, the JVM framework of Oracle Labs for self-optimizing interpreters.
In this talk, we show how a simple interpreter can reach the same order of magnitude of performance as the highly optimizing JVM for Java. We discuss the implementation on top of RPython as well as on top of Java with Truffle so that you can start right away, independent of whether you prefer the Python or JVM ecosystem.
While our own experiments focus on SOM, a little Smalltalk variant to keep things simple, other people have used this approach to improve peek performance of JRuby, or build languages such as JavaScript, R, and Python 3.
This document discusses Thrust, a C++ template library for CUDA that provides highly optimized parallel algorithms and data structures. Thrust aims to mimic the Standard Template Library (STL) and provide containers like vectors and algorithms like sort that work seamlessly on both host and device memory. Key aspects covered include Thrust containers that hide memory management details, iterators that behave like pointers, generic algorithms that support user-defined types and operators, and fancy iterators that enable optimizations like kernel fusion.
The document provides examples of code snippets in C# to demonstrate various OOP concepts like inheritance, polymorphism, delegates, constructors, exception handling, file I/O, and adding a flash item to a website. It also explains XML and DTDs. The code snippets show how to implement inheritance by defining a base Shape class and derived Rectangle class, implement polymorphism by overloading a print method, use delegates to call methods, define default and parameterized constructors, handle exceptions, perform file read/write operations, and add a flash file to an HTML document. The explanation of XML covers internal and external DTD declarations to define document structure.
Handling Exceptions In C & C++ [Part B] Ver 2ppd1961
This document discusses exception handling in C++. It provides an overview of how compilers manage exceptional control flow and how functions are instrumented to handle exceptions. It discusses normal vs exceptional function call flows, and the items involved in stack frames like context, finalization, and unwinding. It also summarizes Meyers' guidelines for exception safety, including using destructors to prevent leaks, handling exceptions in constructors, and preventing exceptions from leaving destructors.
The document discusses transaction-based hardware-software co-verification using emulation. It describes how traditional cycle-based co-verification is slow due to communication overhead between the testbench and emulator. Transaction-based co-verification improves speed by only synchronizing when required and allowing parallel execution. Transactors are used to convert high-level commands from the testbench to a bit-level protocol for the emulator. This allows emulation speeds of tens of MHz, orders of magnitude faster than cycle-based. An example transactor for a virtual memory is presented.
This is the final report for my project as a Technical Student at CERN.
The Intel Xeon/Phi platform is a powerful x86 multi-core engine with a very high-speed memory interface. In its next version it will be able to operate as a stand-alone system with a very high-speed interconnect. This makes it a very interesting candidate for (near) real-time applications such as event-building, event-sorting and event preparation for subsequent processing by high level trigger software algorithms.
This document discusses strategies for task and data parallelism in .NET. It begins with an overview of tasks and how they provide a cheaper alternative to threads for parallelizing work. Various APIs for parallelism are covered, including Parallel Loops, PLINQ, and task continuations. Best practices are provided around uneven work distribution, dependency management, minimizing synchronization, and leveraging lock-free and dataflow patterns. The document concludes with tips on profiling, SIMD, and GPU parallelism.
Adam Sitnik "State of the .NET Performance"Yulia Tsisyk
MSK DOT NET #5
2016-12-07
In this talk Adam will describe how latest changes in.NET are affecting performance.
Adam wants to go through:
C# 7: ref locals and ref returns, ValueTuples.
.NET Core: Spans, Buffers, ValueTasks
And how all of these things help build zero-copy streams aka Channels/Pipelines which are going to be a game changer in the next year.
OpenGL 4.4 provides new features for accelerating scenes with many objects, which are typically found in professional visualization markets. This talk will provide details on the usage of the features and their effect on real-life models. Furthermore we will showcase how more work for rendering a scene can be off-loaded to the GPU, such as efficient occlusion culling or matrix calculations.
Video presentation here: http://on-demand.gputechconf.com/gtc/2014/video/S4379-opengl-44-scene-rendering-techniques.mp4
The document discusses the process for context switching between tasks in Rust. It explains that the current task is grabbed from thread-local storage and its ability to sleep is checked. The next task and cleanup function are prepared. Unsafe transmutes are used to get mutable references to tasks. The task contexts are swapped using a raw operation, placing the scheduler and next task in the proper locations. On the return swap, the cleanup function is immediately run.
Putting a Fork in Fork (Linux Process and Memory Management)David Evans
The document discusses several topics related to computer science class cs4414 at University of Virginia:
- Updates were due Sunday at 11:59pm including progress updates and scheduling design reviews.
- Tuesday's class will feature a guest lecture on authentication using single sign-on.
- The last class covered translation lookaside buffers and paging/segmentation concepts.
- A code sample is shown and analyzed that causes a segmentation fault due to accessing memory outside the allocated space.
- Details are provided on limiting resources and viewing process limits.
Solving Endgames in Large Imperfect-Information Games such as PokerKarel Ha
My master thesis on solving endgames in imperfect-information games.
keywords: algorithmic game theory, imperfect-information games, Nash equilibrium, subgame, endgame, counterfactual regret minimization, Poker
The document discusses distributed programming using Java sockets and RMI. It describes UDP and TCP protocols, with UDP being connectionless and less reliable while TCP is connection-oriented. It then covers socket programming using DatagramSocket and DatagramPacket for UDP, and Socket and input/output streams for TCP. The document also discusses implementing a remote interface using RMI to allow method calls between JVMs.
This document summarizes a research paper that proposes a parallel k-nearest neighbors (kNN) algorithm using OpenCL on a GPU architecture. The key points are:
1) kNN classification is computationally intensive, especially for large datasets, creating a need for parallelization.
2) The authors designed and implemented a parallel kNN algorithm using OpenCL to distribute distance computations across GPU cores.
3) Experimental results on UCI datasets showed the parallel kNN approach improved performance over a serial kNN implementation.
Kenneth presented a summary of his participation in the Kaggle TGS Salt Identification Challenge. He discussed the top solutions which utilized techniques like ResNeXt and ResNet backbones, feature pyramid attention, Lovasz loss, and test time augmentation. Kenneth's own solution ranked 1482nd, using a U-Net architecture with Lovasz loss and test time flipping. He analyzed techniques that did and did not work well for this salt segmentation task from radar images.
The document discusses the Rust runtime and how it interfaces with the Linux kernel to spawn new processes. It covers how fork() is implemented at the libc level and how it ultimately triggers a system call by generating an interrupt. It also discusses entering supervisor mode in the kernel and recaps some PS3 benchmarking results.
Efficient Dynamic Scheduling Algorithm for Real-Time MultiCore Systems iosrjce
Imprecise computation model is used in dynamic scheduling algorithm having heuristic function to
schedule task sets. A task is characterized by ready time, worst case computation time, deadline and resource
requirements. A task failing to meet its deadline and resource requirements on time is split into mandatory part
and optional part. These sub-tasks of a task can execute concurrently on multiple cores, thus achieving
parallelization provided by the multi-core system. Mandatory part produces acceptable results while optional
part refines the result further. To study the effectiveness of proposed scheduling algorithm, extensive simulation
studies have been carried out. Performance of proposed scheduling algorithm is compared with myopic and
improved myopic scheduling algorithm. The simulation studies shows that schedulability of task split myopic
algorithm is always higher than myopic and improved myopic algorithm.
The document outlines the schedule and content for a university computer science class. It includes an introduction to scheduling, a kernel timer winner, scheduling demos from student teams, a discussion of scheduling strategies like first-come first-served and round-robin, and a review of priority scheduling. It also announces details about an upcoming midterm exam and shows the results of a Rust program performance test on different laptops.
This document discusses Q-normalization, a method for normalizing gene expression data. It presents parallel implementations of Q-normalization using shared memory, message passing, and GPU architectures. Benchmarking shows the GPU implementation provides a 5.5x speedup over the sequential CPU version for processing large gene expression datasets. The shared memory implementation provides a 2.9x total speedup, while the message passing version is suitable for distributed memory clusters.
Compressed Sensing using Generative Modelkenluck2001
This document summarizes Kenneth Emeka Odoh's presentation on using generative models for compressed sensing. It introduces compressed sensing and describes how generative models like variational autoencoders (VAEs) and generative adversarial networks (GANs) can be used to solve the underdetermined linear systems that arise in compressed sensing. The document also compares VAEs to standard autoencoders and discusses how generative models may require fewer training examples due to regularization imposing structure on the problem.
This document summarizes lecture slides from a university computer science class on operating system processes and virtual memory. The slides covered:
- Last week's discussion of process creation in Rust and the fork system call.
- The plan for this week's lectures, including how the kernel makes processes and diving into the fork.c source code.
- An overview of virtual memory and how it provides memory isolation between processes using paging and segmentation.
- Details of how x86 processors implement virtual memory using segmentation and paging tables, address translation, and handling of page faults.
- The history and evolution of virtual memory from early mainframe systems to modern desktop processors.
Building High-Performance Language Implementations With Low EffortStefan Marr
This talk shows how languages can be implemented as self-optimizing interpreters, and how Truffle or RPython go about to just-in-time compile these interpreters to efficient native code.
Programming languages are never perfect, so people start building domain-specific languages to be able to solve their problems more easily. However, custom languages are often slow, or take enormous amounts of effort to be made fast by building custom compilers or virtual machines.
With the notion of self-optimizing interpreters, researchers proposed a way to implement languages easily and generate a JIT compiler from a simple interpreter. We explore the idea and experiment with it on top of RPython (of PyPy fame) with its meta-tracing JIT compiler, as well as Truffle, the JVM framework of Oracle Labs for self-optimizing interpreters.
In this talk, we show how a simple interpreter can reach the same order of magnitude of performance as the highly optimizing JVM for Java. We discuss the implementation on top of RPython as well as on top of Java with Truffle so that you can start right away, independent of whether you prefer the Python or JVM ecosystem.
While our own experiments focus on SOM, a little Smalltalk variant to keep things simple, other people have used this approach to improve peek performance of JRuby, or build languages such as JavaScript, R, and Python 3.
This document discusses Thrust, a C++ template library for CUDA that provides highly optimized parallel algorithms and data structures. Thrust aims to mimic the Standard Template Library (STL) and provide containers like vectors and algorithms like sort that work seamlessly on both host and device memory. Key aspects covered include Thrust containers that hide memory management details, iterators that behave like pointers, generic algorithms that support user-defined types and operators, and fancy iterators that enable optimizations like kernel fusion.
The document provides examples of code snippets in C# to demonstrate various OOP concepts like inheritance, polymorphism, delegates, constructors, exception handling, file I/O, and adding a flash item to a website. It also explains XML and DTDs. The code snippets show how to implement inheritance by defining a base Shape class and derived Rectangle class, implement polymorphism by overloading a print method, use delegates to call methods, define default and parameterized constructors, handle exceptions, perform file read/write operations, and add a flash file to an HTML document. The explanation of XML covers internal and external DTD declarations to define document structure.
Handling Exceptions In C & C++ [Part B] Ver 2ppd1961
This document discusses exception handling in C++. It provides an overview of how compilers manage exceptional control flow and how functions are instrumented to handle exceptions. It discusses normal vs exceptional function call flows, and the items involved in stack frames like context, finalization, and unwinding. It also summarizes Meyers' guidelines for exception safety, including using destructors to prevent leaks, handling exceptions in constructors, and preventing exceptions from leaving destructors.
The document discusses transaction-based hardware-software co-verification using emulation. It describes how traditional cycle-based co-verification is slow due to communication overhead between the testbench and emulator. Transaction-based co-verification improves speed by only synchronizing when required and allowing parallel execution. Transactors are used to convert high-level commands from the testbench to a bit-level protocol for the emulator. This allows emulation speeds of tens of MHz, orders of magnitude faster than cycle-based. An example transactor for a virtual memory is presented.
The document discusses Node.js and provides instructions for installing Node.js via different methods:
1) Homebrew can be used to install Node.js on OSX by running "brew install node.js".
2) nDistro allows creating and installing Node.js distributions within seconds by specifying module and Node binary version dependencies in a .ndistro file.
3) Node.js can be compiled from source by cloning the Node.js repository via git or downloading the source, running configuration, make, and make install commands.
This document provides an agenda and overview for an introduction to OpenCL course. The agenda includes lectures on understanding host programs, kernel programs, memory models, and optimization. Course materials include OpenCL reference cards, specifications, and exercises. An introduction to OpenCL explains that it is an open standard for parallel programming across heterogeneous systems like CPUs and GPUs. The OpenCL platform model includes devices like GPUs that are divided into compute units and processing elements. Kernels define work-items that execute problems in parallel over a domain.
This document provides an overview of CUDA C/C++ basics for processing data in parallel on GPUs. It discusses:
- The CUDA architecture which exposes GPU parallelism for general-purpose computing while retaining performance.
- The CUDA programming model which uses a grid of thread blocks, with each block containing a group of threads that execute concurrently.
- Key CUDA C/C++ concepts like declaring device functions, launching kernels, managing host and device memory, and using thread and block indexes to parallelize work across threads and blocks.
- A simple example of vector addition to demonstrate parallel execution using threads and blocks, with indexing to map threads/blocks to problem elements.
This document provides an overview of Objective-C and discusses several key concepts:
1. It begins with instructions for setting up the Objective-C development environment on Linux, Mac OS X, and Windows systems.
2. A "Hello World" example program is presented to demonstrate the basic structure of an Objective-C application and how to print output.
3. Object-oriented concepts like classes, inheritance, and polymorphism are explained through examples showing how to define classes with interfaces and implementations, create class instances, and call methods.
4. Additional Objective-C features are demonstrated like method parameters, constructors, access privileges, class methods, and exceptions handling.
This document provides an overview and introduction to OpenCL, including:
- OpenCL allows for portable, parallel programming across heterogeneous systems like CPUs and GPUs.
- The OpenCL platform model uses kernels executed across a domain of work-items to parallelize work, with work organized into work-groups for synchronization.
- Memory is explicitly managed, with private, local, and global memory spaces accessible to kernels via the memory model.
- The host program sets up the OpenCL context and devices, builds kernels, manages memory objects, and submits commands via command queues to execute kernels and synchronize work.
HSA enables more efficient compilation of high-level programming interfaces like OpenACC and C++AMP. For OpenACC, HSA provides flexibility in implementing data transfers and optimizing nested parallel loops. For C++AMP, HSA allows efficient compilation from an even higher level interface where GPU data and kernels are modeled as C++ containers and lambdas, without needing to specify data transfers. Overall, HSA aims to reduce boilerplate code for heterogeneous programming and provide better portability across devices.
GPU programing
The Brick Wall -- UC Berkeley's View
Power Wall: power expensive, transistors free
Memory Wall: Memory slow, multiplies fast ILP Wall: diminishing returns on more ILP HW
The document summarizes a lecture on parallel computing with CUDA (Compute Unified Device Architecture). It introduces CUDA as a parallel programming model for GPUs, covering key concepts like memory architecture, host-GPU workload partitioning, programming paradigm, and programming examples. It then outlines the agenda, benefits of GPU computing, and provides details on CUDA programming interfaces, kernels, threads, blocks, and memory hierarchies. Finally, it lists some lab exercises on CUDA programming including HelloWorld, matrix multiplication, and parallel sorting algorithms.
Here is a recursive C function that solves the Tower of Hanoi problem:
#include <stdio.h>
void towerOfHanoi(int n, char from_rod, char to_rod, char aux_rod)
{
if (n == 0)
return;
towerOfHanoi(n-1, from_rod, aux_rod, to_rod);
printf("Move disk %d from rod %c to rod %c\n", n, from_rod, to_rod);
towerOfHanoi(n-1, aux_rod, to_rod, from_rod);
}
int main()
{
int
Presentation from DICE Coder's Day (2010 November) by Andreas Fredriksson in the Frostbite team.
Goes into detail about Scope Stacks, which are a systems programming tool for memory layout that provides
• Deterministic memory map behavior
• Single-cycle allocation speed
• Regular C++ object life cycle for objects that need it
This makes it very suitable for games.
The document discusses dynamic memory allocation in C programming. It provides details on four main library functions - malloc(), calloc(), free(), and realloc() - that allow dynamic memory allocation. malloc() allocates memory without initialization, calloc() allocates and initializes memory to zero, free() de-allocates previously allocated memory, and realloc() changes the size of previously allocated memory. Examples are given to demonstrate the usage of each function. The document also discusses self-referential structures, bit fields, typedef, scope of variables, and storage classes in C.
Samsung developed a WebCL prototype to enable general purpose parallel programming across heterogeneous processing elements from web applications. The prototype uses ECMAScript APIs to interact with OpenCL in a similar way as WebGL. It aims to closely resemble OpenCL APIs to preserve familiarity and facilitate adoption as OpenCL evolves. The prototype provides functionality for compute contexts, platform/device queries, kernel building, buffer/image operations, and synchronization. It implements blocking and non-blocking APIs and provides a JavaScript class hierarchy that maps to the OpenCL object model. Demo applications show performance improvements using the prototype for tasks like N-body simulation and deformation calculation.
This document introduces PyOpenCL and provides examples for using it to perform GPU computing via Python. It begins with an overview of GPU computing and OpenCL. It then discusses setting up the PyOpenCL environment on different platforms like Windows, MacOS, and Linux. Examples shown include printing "Hello Taiwan" on the GPU, performing arithmetic operations on arrays in parallel, and image processing tasks like grayscale conversion and blurring using OpenCL data types and memory models. Atomic functions and synchronization are also covered.
C# classes allow for modularity, data encapsulation, inheritance, and polymorphism. They act as blueprints for generating object instances. The document discusses key object-oriented programming concepts in C# like encapsulation, inheritance, polymorphism, casting, exception handling, garbage collection, interfaces, collections, comparables, and delegates. It provides examples to illustrate concepts like shallow cloning using ICloneable, implementing IComparable, overloading operators, and using XML documentation comments.
Speaker: Jacob Aae Mikkelsen
Once you have successfully developped your application in Grails, Ratpack or your other favorite framework, you would like to see it deployed as fast and painless as possible, right?
This talk will cover some of the supporting cast members of a succesful modern infrastructure, that developers can understand and use efficiently, and with good DevOps practices.
Key elements are
Docker
Infrastructure as Code
Container Orchestration
The demo-goods will hopefully be on our side, as this talk includes quite some live demos!
Skybuffer AI: Advanced Conversational and Generative AI Solution on SAP Busin...Tatiana Kojar
Skybuffer AI, built on the robust SAP Business Technology Platform (SAP BTP), is the latest and most advanced version of our AI development, reaffirming our commitment to delivering top-tier AI solutions. Skybuffer AI harnesses all the innovative capabilities of the SAP BTP in the AI domain, from Conversational AI to cutting-edge Generative AI and Retrieval-Augmented Generation (RAG). It also helps SAP customers safeguard their investments into SAP Conversational AI and ensure a seamless, one-click transition to SAP Business AI.
With Skybuffer AI, various AI models can be integrated into a single communication channel such as Microsoft Teams. This integration empowers business users with insights drawn from SAP backend systems, enterprise documents, and the expansive knowledge of Generative AI. And the best part of it is that it is all managed through our intuitive no-code Action Server interface, requiring no extensive coding knowledge and making the advanced AI accessible to more users.
Taking AI to the Next Level in Manufacturing.pdfssuserfac0301
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
GraphRAG for Life Science to increase LLM accuracyTomaz Bratanic
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
Digital Marketing Trends in 2024 | Guide for Staying AheadWask
https://www.wask.co/ebooks/digital-marketing-trends-in-2024
Feeling lost in the digital marketing whirlwind of 2024? Technology is changing, consumer habits are evolving, and staying ahead of the curve feels like a never-ending pursuit. This e-book is your compass. Dive into actionable insights to handle the complexities of modern marketing. From hyper-personalization to the power of user-generated content, learn how to build long-term relationships with your audience and unlock the secrets to success in the ever-shifting digital landscape.
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
Your One-Stop Shop for Python Success: Top 10 US Python Development Providersakankshawande
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
Programming Foundation Models with DSPy - Meetup SlidesZilliz
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
A Comprehensive Guide to DeFi Development Services in 2024Intelisync
DeFi represents a paradigm shift in the financial industry. Instead of relying on traditional, centralized institutions like banks, DeFi leverages blockchain technology to create a decentralized network of financial services. This means that financial transactions can occur directly between parties, without intermediaries, using smart contracts on platforms like Ethereum.
In 2024, we are witnessing an explosion of new DeFi projects and protocols, each pushing the boundaries of what’s possible in finance.
In summary, DeFi in 2024 is not just a trend; it’s a revolution that democratizes finance, enhances security and transparency, and fosters continuous innovation. As we proceed through this presentation, we'll explore the various components and services of DeFi in detail, shedding light on how they are transforming the financial landscape.
At Intelisync, we specialize in providing comprehensive DeFi development services tailored to meet the unique needs of our clients. From smart contract development to dApp creation and security audits, we ensure that your DeFi project is built with innovation, security, and scalability in mind. Trust Intelisync to guide you through the intricate landscape of decentralized finance and unlock the full potential of blockchain technology.
Ready to take your DeFi project to the next level? Partner with Intelisync for expert DeFi development services today!
Main news related to the CCS TSI 2023 (2023/1695)Jakub Marek
An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
Letter and Document Automation for Bonterra Impact Management (fka Social Sol...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on automated letter generation for Bonterra Impact Management using Google Workspace or Microsoft 365.
Interested in deploying letter generation automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Trusted Execution Environment for Decentralized Process MiningLucaBarbaro3
Presentation of the paper "Trusted Execution Environment for Decentralized Process Mining" given during the CAiSE 2024 Conference in Cyprus on June 7, 2024.
Nunit vs XUnit vs MSTest Differences Between These Unit Testing Frameworks.pdfflufftailshop
When it comes to unit testing in the .NET ecosystem, developers have a wide range of options available. Among the most popular choices are NUnit, XUnit, and MSTest. These unit testing frameworks provide essential tools and features to help ensure the quality and reliability of code. However, understanding the differences between these frameworks is crucial for selecting the most suitable one for your projects.
Introduction of Cybersecurity with OSS at Code Europe 2024Hiroshi SHIBATA
I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems.
The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS.
Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application.
I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.
OpenCL allows specifying a host pointer when we declare a object. In this case copy to device is invoked when kernel is enqueued.
These memory flags are used to specify how a buffer is allocated
Point to mention here is that OpenCL event handling capabilities are not available when we do copying using the host pointer parameter of clcreatebuffer because the copy is done implicitly.So if profiling is going to be used, this function call is necessary instead of passing host pointers
Similar to clEnqueueWriteBuffer
Simple data parallel algorithm and exampleEach work item handles one pixel.
Pseudo-code for OpenCL kernels
This is a simple example and improvements are possible in the accessing of memory because reads will not be perfectly aligned.However the aim of this example is to show how a data – space like a image can be mapped to a grid of work items and how each work item can process its piece of data independently of other work items.
This is the essential boiler plate code that can be reused for any OpenCL program.This creates the context, command queue and picks a device
IP and OP buffers
These steps can be reused for all OpenCL functions.
Execute kernel on GPU
Read back output
By taking the difference between start and end times, we can check execution duration of kernels without overhead as gettimeofday would have
Simple serial 3-loop matrix multiplication
OpenCL getting started guide example.This example also shows how each work item can use its own section of input data (Matrix A and B ) to calculate the result for its own position in Matrix C