Memory Leak in Windows CPU OCL 1.2/2.0, not so much GPU 1.2

Hello world,

I'm developing an asynchronous Windows application and have noticed a strange loss of system memory. My application internally tracks memory usage, and when not using OpenCL at all it matches what is reported by the system through taskmgr. What's curious is the memory leak is more or less depending on what OpenCL version and device I use. Summarizing what taskmgr reports:

No OpenCL (vanilla C code) - ~8MB
OpenCL 2.0 Experimental CPU ~ 1.2 GB
OpenCL 1.2 CPU ~ 350 MB
OpenCL 1.2 GPU (HD 4600) ~ 40 MB

two dimensional array offload issue

i have two dimensional dynamic array that i offload to phi. i dont really pass any data all i want is to allocate mem via transfer and access that mem via nocopy each iteration later on

void foo()

unsigned int ** twoDimArray = new ... etc ... [n*m]

#pragma offload_transfer target(mic:MIC_DEV) in(twoDimArray :length(n*m) alloc_if(1) free_if(0))

while (condition) {

//nocopy offload each iteration of external loop
#pragma offload target(mic:MIC_DEV) nocopy(twoDimArray :length(n*m) alloc_if(0) free_if(0))


mic0 reset failed


I am trying to make coprocessors work on Ubuntu 14.04 but I got stuck with error: mic0 reset failed. I have tried several methods noted in this form but I was not able to make it work. So, I am seeking help to make my mic work. Please, find my micbug as attachment.

Thank you.


preventing execution of remainder loop on xeon phi coprocessor

Hey everyone, consider the following sample code below. 

compiling with ifort -O3 -align array64byte -openmp -vec-report6 spits out something to the effect that nlist is aligned, the SIMD generated vectorization, and position is 64 bit indexed in the offloaded inner loop at line 93. However in the remainder loop, as we expect, nothing is aligned but the remainder code is vectorized. The !dir$ vector aligned prevents the creation of a peel loop like want.

Quick Installation Guide for Graphics Performance Analyzers on Windows with Intel® INDE

Intel® INDE provides a comprehensive toolset for developing applications targeting both CPU and GPUs, enriching the development experience of a game or media developer. Yet, if you got used to work with the legacy Intel® Graphics Performance Analyzers or if you just want to get started using those tools quickly, you can follow these steps and install only the Graphics Performance Analyzers components of Intel® INDE.

Go to the Intel® INDE Web page, select the edition you want to download and hit Download link:

  • Desenvolvedores
  • Professores
  • Estudantes
  • Microsoft Windows* (XP, Vista, 7)
  • Microsoft Windows* 8
  • Desenvolvimento de jogos
  • Windows*
  • C/C++
  • Avançado
  • Principiante
  • Intermediário
  • Intel® Integrated Native Developer Experience (INDE)
  • Analisadores de desempenho gráfico
  • GPA Analyzer
  • GPA
  • intel gpa system analyzer
  • Frame Analyzer
  • System Analyzer
  • Intel Frame Debugger
  • Microsoft DirectX*
  • OpenCL*
  • Computação perceptiva
  • Ferramentas de desenvolvimento
  • Desenvolvimento de jogos
  • Gráficos
  • Área de trabalho do Microsoft Windows* 8
  • Announcing Intel® Data Analytics Acceleration Library 2016 Beta

    We are pleased to announce the release of Intel® Data Analytics Acceleration Library 2016 Beta!

    Intel® Data Analytics Acceleration Library is a C++ and Java API library of optimized analytics building blocks for all data analysis stages, from data acquisition to data mining and machine learning. It is a library essential for engineering high performance data application solutions.

  • Desenvolvedores
  • Parceiros
  • Professores
  • Estudantes
  • Apple OS X*
  • Linux*
  • Microsoft Windows* (XP, Vista, 7)
  • Microsoft Windows* 8
  • Cliente empresarial
  • Internet das coisas
  • Servidor
  • Windows*
  • C/C++
  • Java*
  • Avançado
  • Principiante
  • Intermediário
  • Bibliotecas
  • Intel® Data Analytics Acceleration Library
  • Big Data
  • Ferramentas de desenvolvimento



    While talking to a very intelligent but non-engineer colleague, I found myself needing to explain the threading and other components of the Intel® Xeon Phi™ ⅹ100 and ⅹ200 architectures. The first topic that came up was hyper-threading, and more specifically, the coprocessor’s version of hyper-threading. Wracking my brain, I finally hit upon an analogy that seemed to suit: the common kitchen.

    Assine o Professores