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Intel Xeon Phi Coprocessor: A Guide to High Performance Computing with Intel MIC Architecture



Intel Xeon Phi Coprocessor High Performance Programming 1




Are you looking for a way to boost the performance of your applications that require high levels of parallelism and vectorization? Do you want to take advantage of the latest hardware innovations from Intel? If so, you might be interested in learning more about the Intel Xeon Phi coprocessor, a new type of device that can deliver unprecedented levels of computing power and efficiency.




intel xeon phi coprocessor high performance programming 1


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In this article, we will introduce you to the Intel Xeon Phi coprocessor, its architecture, its programming models and tools, and its optimization strategies and examples. We will also answer some frequently asked questions about this exciting technology. By the end of this article, you will have a better understanding of how to program for and optimize for the Intel Xeon Phi coprocessor.


What is Intel Xeon Phi Coprocessor?




The Intel Xeon Phi coprocessor is a PCI Express card that contains many cores based on the Intel Many Integrated Core (Intel MIC) architecture. It is designed to extend the reach of applications that have demonstrated the ability to fully utilize the scaling capabilities of Intel Xeon processor-based systems and fully exploit available processor vector capabilities or memory bandwidth.


The Intel Xeon Phi coprocessor can be used as a standalone device or as an accelerator for a host system that runs an Intel Xeon processor. It can run Linux-based operating systems and applications natively, or offload portions of code from the host system. It can also communicate with other Intel Xeon Phi coprocessors or host systems using standard network protocols such as MPI.


The Intel Many Integrated Core Architecture




The Intel MIC architecture is the foundation of the Intel Xeon Phi coprocessor. It consists of up to 61 cores, each with four hardware threads, running at frequencies up to 1.2 GHz. Each core has a 512-bit wide vector processing unit that supports 16 single-precision or eight double-precision floating-point operations per cycle. Each core also has a 32 KB L1 instruction cache, a 32 KB L1 data cache, and a 512 KB L2 cache. The cores are connected by a high-speed bidirectional ring interconnect that enables data transfers between cores and memory.


The memory subsystem of the Intel MIC architecture consists of up to 16 GB of GDDR5 memory with a peak bandwidth of 352 GB/s. The memory is distributed across eight memory controllers, each with two channels. The memory controllers are also connected by the ring interconnect and can access any memory location on the device. The memory subsystem supports ECC protection and memory page migration.


The I/O subsystem of the Intel MIC architecture consists of a PCI Express x16 Gen3 interface that connects the device to the host system or other devices. It also supports six external ports that can be configured as PCIe x1, x4, or x16, or as Intel Quick Path Interconnect (Intel QPI) links. These ports can be used for connecting multiple Intel Xeon Phi coprocessors or other systems in a cluster configuration.


The Benefits of Intel Xeon Phi Coprocessor




The Intel Xeon Phi coprocessor offers several benefits for high performance computing applications, such as:


  • High performance: The Intel Xeon Phi coprocessor can deliver up to 1.2 teraflops of double-precision peak performance and up to 2.4 teraflops of single-precision peak performance. It can also achieve high performance per watt and per dollar compared to other solutions.



  • High scalability: The Intel Xeon Phi coprocessor can scale up to thousands of cores and terabytes of memory in a single system or cluster. It can also leverage the existing infrastructure and software stack of Intel Xeon processor-based systems, such as operating systems, compilers, libraries, debuggers, profilers, and cluster management tools.



  • High programmability: The Intel Xeon Phi coprocessor supports a familiar and proven threaded, scalar-vector programming model that is compatible with the Intel Xeon processor. It also supports multiple programming languages, such as C, C++, Fortran, Python, and R, and multiple programming paradigms, such as OpenMP, MPI, TBB, Cilk Plus, and OpenCL.



How to Program for Intel Xeon Phi Coprocessor?




Programming for the Intel Xeon Phi coprocessor is similar to programming for the Intel Xeon processor, but with some differences and considerations. In this section, we will introduce the programming models and tools that are available for the Intel Xeon Phi coprocessor.


The Programming Models




The Intel Xeon Phi coprocessor supports three main programming models: native mode, offload mode, and symmetric mode.


Native Mode




In native mode, the Intel Xeon Phi coprocessor runs as a standalone device with its own operating system and applications. The applications are compiled and executed directly on the device, without involving the host system. The applications can use standard Linux commands and libraries, as well as Intel-specific extensions and libraries. The applications can also communicate with other devices or systems using network protocols such as MPI or TCP/IP.


Native mode is suitable for applications that can run entirely on the device and do not require frequent data transfers or interactions with the host system. Native mode can also simplify the development and debugging process by avoiding the complexity of offloading code or data between different devices.


Offload Mode




In offload mode, the Intel Xeon Phi coprocessor acts as an accelerator for the host system that runs an Intel Xeon processor. The applications are compiled and executed on the host system, but some portions of code or data are offloaded to the device using directives or APIs. The offloaded code or data are executed on the device in parallel with the host code or data. The offloaded code or data are then returned to the host system when they are done.


Offload mode is suitable for applications that have some parts that can benefit from the parallelism and vectorization of the device, but also have some parts that depend on the features or performance of the host system. Offload mode can also enable incremental porting and optimization of existing applications by identifying and offloading hotspots or bottlenecks.


Symmetric Mode




In symmetric mode, the Intel Xeon Phi coprocessor and the host system run as peers in a cluster configuration. The applications are compiled and executed on both devices using a common programming model such as MPI. The applications can communicate and synchronize with each other using standard network protocols such as MPI or TCP/IP.


Symmetric mode is suitable for applications that can scale across multiple devices or systems and do not have strong dependencies or preferences between them. Symmetric mode can also maximize the utilization and efficiency of both devices by balancing the workload and resources among them.


The Programming Tools




The Intel Xeon Phi coprocessor supports a variety of programming tools that can help developers create, debug, optimize, and deploy their applications. Some of these tools are:


Intel Compiler and Libraries




The Intel Compiler is a high-performance compiler that can generate optimized code for both the Intel Xeon processor and the Intel Xeon Phi coprocessor. It supports multiple programming languages such as C, C++, Fortran, Python, and R, and multiple programming paradigms such as OpenMP, MPI, TBB, Cilk I'm continuing to write the article on the topic of "intel xeon phi coprocessor high performance programming 1" as you requested. Here is the next part of the article with HTML formatting. ```html Plus, and OpenCL. It also supports automatic or explicit vectorization and parallelization, as well as offloading directives and APIs for the Intel Xeon Phi coprocessor.


The Intel Libraries are a set of high-performance libraries that provide optimized functions for math, threading, data analysis, media, and encryption. Some of these libraries are Intel Math Kernel Library (Intel MKL), Intel Threading Building Blocks (Intel TBB), Intel Integrated Performance Primitives (Intel IPP), Intel Data Analytics Acceleration Library (Intel DAAL), and Intel Media SDK. These libraries can run natively or offload to the Intel Xeon Phi coprocessor.


Intel MPI Library




The Intel MPI Library is a high-performance implementation of the Message Passing Interface (MPI) standard that enables communication and synchronization among multiple processes running on different devices or systems. It supports various network fabrics, such as InfiniBand, Ethernet, and Intel QPI. It also supports various features, such as process pinning, collective tuning, fault tolerance, and hybrid programming with OpenMP or Intel TBB. The Intel MPI Library can run natively or offload to the Intel Xeon Phi coprocessor.


Intel VTune Amplifier XE




The Intel VTune Amplifier XE is a powerful performance analysis tool that can help developers identify and eliminate performance bottlenecks in their applications. It can collect various types of data, such as CPU utilization, memory bandwidth, cache misses, vectorization efficiency, synchronization overhead, and power consumption. It can also provide various types of views, such as timeline, hotspot, call tree, event count, and roofline. The Intel VTune Amplifier XE can analyze applications running on the host system or on the Intel Xeon Phi coprocessor.


How to Optimize for Intel Xeon Phi Coprocessor?




Optimizing for the Intel Xeon Phi coprocessor is similar to optimizing for the Intel Xeon processor, but with some differences and considerations. In this section, we will introduce some optimization strategies and examples that are relevant for the Intel Xeon Phi coprocessor.


The Optimization Strategies




The optimization strategies for the Intel Xeon Phi coprocessor can be summarized as follows:


  • Vectorize: Use the full width of the vector processing unit by ensuring that your data structures are aligned and contiguous, your loops are regular and free of dependencies, and your compiler options are set correctly. You can also use explicit vector intrinsics or directives to guide the compiler or write your own vector code.



  • Parallelize: Use the full number of cores and threads by ensuring that your algorithms are scalable and load-balanced, your synchronization is minimal and efficient, and your thread affinity is controlled. You can also use explicit parallel constructs or directives to guide the compiler or write your own parallel code.



  • Optimize memory: Use the full bandwidth of the memory subsystem by ensuring that your data access patterns are sequential and cache-friendly, your data transfers are minimized and overlapped, and your memory allocation is aligned and distributed. You can also use explicit memory hints or directives to guide the compiler or write your own memory code.



The Optimization Examples




To illustrate some of these optimization strategies in action, we will use two simple but representative examples: matrix multiplication and N-body simulation.


Matrix Multiplication




Matrix multiplication is a common operation in many scientific and engineering applications. It involves multiplying two matrices A and B to produce a matrix C, such that C[i][j] = sum(A[i][k] * B[k][j]) for all i and j.


The following code shows a naive implementation of matrix multiplication in C:


```c // Naive matrix multiplication void matmul(double A, double B, double C, int N) int i,j,k; for (i = 0; i This code can be optimized for the Intel Xeon Phi coprocessor by applying the following techniques:


  • Vectorize: Use the __declspec(align(64)) attribute to align the matrices to 64 bytes, which is the size of a vector register. Use the #pragma vector aligned directive to inform the compiler that the matrices are aligned. Use the #pragma ivdep directive to inform the compiler that there are no loop-carried dependencies. Use the -O3 -xMIC-AVX512 compiler options to enable high-level optimizations and code generation for the Intel Xeon Phi coprocessor.



  • Parallelize: Use the #pragma omp parallel for collapse(2) directive to parallelize the outer two loops using OpenMP. This will create a team of threads that will share the iteration space of the loops. The collapse(2) clause will collapse the two loops into one, which can improve load balancing. The reduction(+:C[i][j]) clause will perform a parallel reduction on the C[i][j] variable, which can avoid race conditions.



  • Optimize memory: Use the #pragma omp simd directive to enable explicit SIMD vectorization for the inner loop. This will generate vector instructions that can load and store multiple elements of the matrices at once, which can improve memory bandwidth. Use the #pragma prefetch A directive to prefetch the elements of matrix A into the cache, which can reduce cache misses. Use the numactl --membind=1 --cpunodebind=1 command to bind the memory allocation and execution to node 1 of the Intel Xeon Phi coprocessor, which can improve memory locality.



The following code shows an optimized implementation of matrix multiplication in C:


```c // Optimized matrix multiplication void matmul(double A, double B, double C, int N) int i,j,k; // Align matrices to 64 bytes __declspec(align(64)) double A, B, C; // Parallelize outer two loops with OpenMP #pragma omp parallel for collapse(2) reduction(+:C[i][j]) for (i = 0; i This code can achieve significant speedup over the naive code when running on the Intel Xeon Phi coprocessor.


N-Body Simulation




N-body simulation is another common operation in many scientific and engineering applications. It involves simulating the motion of N particles that interact with each other through gravitational forces. It requires computing the force and acceleration for each particle, and then updating its position and velocity.


The following code shows a naive implementation of N-body simulation in C:


```c // Naive N-body simulation void nbody(double *x, double *y, double *z, double *vx, double *vy, double *vz, double *m, int N, double dt) int i,j; double dx,dy,dz,dist,s,c; // Compute forces for (i = 0; i This code can be optimized for the Intel Xeon Phi coprocessor by applying the following techniques:


  • Vectorize: Use the __declspec(align(64)) attribute to align the arrays to 64 bytes, which is the size of a vector register. Use the #pragma vector aligned directive to inform the compiler that the arrays are aligned. Use the #pragma ivdep directive to inform the compiler that there are no loop-carried dependencies. Use the -O3 -xMIC-AVX512 compiler options to enable high-level optimizations and code generation for the Intel Xeon Phi coprocessor.



  • Parallelize: Use the #pragma omp parallel for directive to parallelize the outer loop using OpenMP. This will create a team of threads that will share the iteration space of the loop. The private(i,j,dx,dy,dz,dist,s,c) clause will declare the variables as private to each thread, which can avoid false sharing. The reduction(+:vx[i],vy[i],vz[i]) clause will perform a parallel reduction on the velocity arrays, which can avoid race conditions.



  • Optimize memory: Use the #pragma omp simd directive to enable explicit SIMD vectorization for the inner loop. This will generate vector instructions that can load and store multiple elements of the arrays at once, which can improve memory bandwidth. Use the #pragma unroll_and_jam(4) directive to unroll and fuse the two loops, which can improve cache reuse and reduce loop overhead. Use the numactl --membind=1 --cpunodebind=1 command to bind the memory allocation and execution to node 1 of the Intel Xeon Phi coprocessor, which can improve memory locality.



The following code shows an optimized implementation of N-body simulation in C:


```c // Optimized N-body simulation void nbody(double *x, double *y, double *z, double *vx, double *vy, double *vz, double *m, int N, double dt) int i,j; double dx,dy,dz,dist,s,c; // Align arrays to 64 bytes __declspec(align(64)) double *x, *y, *z, *vx, *vy, *vz, *m; // Parallelize outer loop with OpenMP #pragma omp parallel for private(i,j,dx,dy,dz,dist,s,c) reduction(+:vx[i],vy[i],vz[i]) for (i = 0; i This code can achieve significant speedup over the naive code when running on the Intel Xeon Phi coprocessor.


Conclusion




In this article, we have introduced you to the Intel Xeon Phi coprocessor, a new type of device that can deliver unprecedented levels of computing power and efficiency for high performance computing applications. We have also shown you how to program for and optimize for the Intel Xeon Phi coprocessor using familiar and proven programming models and tools.


We hope that this article has sparked your interest and curiosity in exploring the potential of the Intel Xeon Phi coprocessor for your own applications. If you want to learn more, you can visit the following resources:


  • The Intel Xeon Phi Coprocessor Developer's Quick Start Guide, which provides a comprehensive overview of the device, its software, and its programming.



  • The Intel Xeon Phi Coprocessor High Performance Programming book, which provides a detailed and practical guide to the device, its architecture, its programming, and its optimization.



  • The Intel Developer Zone website, which provides various articles, tutorials, videos, webinars, forums, and blogs on the device and its related topics.



FAQs




Here are some frequently asked questions about the Intel Xeon Phi coprocessor:



  • What are the differences between the Intel Xeon Phi coprocessor and the Intel Xeon processor?



The Intel Xeon Phi coprocessor and the Intel Xeon processor are both based on the x86 architecture, but they h


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