The programmable Xilinx 7020 is flexible and can contain 8Gbps link to Epiphany-III and RISC-V core or e-link and Analog Devices HDMI or something else?
All the recipes to build a linux distribution in one place meta-exotic: Yocto layer to support cross compiler creation for exotic or foreign microcontrollers https://wiki.yoctoproject.org/wiki/Regression_Test In no particular order:
Contributors or sponsors for this work are always welcome!
Taxonomy first published by Michael J Flynn (1972) now extended:
MIMD: Multiple instruction streams, multiple data streams MISD: Multiple instruction streams, single data stream Examples:
MPMD: possibly unrelated work is split up into multiple programmes and run simultaneously on multiple processors with different input in order to obtain multiple result SIMD: Single Instruction Stream, Multiple Data Streams. Standardised in OpenMP 4.0 Examples:
SISD: Single Instruction Stream, Single Data Stream SPMD: tasks are split up and run simultaneously on multiple processors with different input in order to obtain results faster. APL: array-oriented programming language that uses pictorial symbols for its language constructs BSP: library to assist in running tasks in a parallel processing system C++AMP: a Microsoft library built on DirectX 11 CAL: is a dataflow language geared towards for example multimedia processing Erlang: a programming language originally developed at the Ericsson Computer Science Laboratory MPI: specification used in a distributed memory model Example API use:
OpenCL: using task-based and data-based parallelism https://www.khronos.org/opencl/
Example data transfer
OpenHMPP: provides extensions for hardware accelerators PAL: library for coprocessors and parallel machines https://github.com/parallella/oh
Example host application
The compiler preprocessor is given additional instructions to identify parts of the software that can be run in parallel
Additional software libraries are provided that enable the application code to run in parallel
Example (http://sc14.supercomputing.org/program/tutorials.html):
A Programming Language: is a high level, concise, array-oriented programming language that uses pictorial symbols for its language constructs also known as an Array Manipulation Language Back to:
Bulk Synchronous Parallel: library to assist in running tasks in a parallel processing system Back to:
C++ Accelerated Massive Parallelism: a Microsoft library built on DirectX 11 used to exploit accelerator hardware Back to:
Cal Actor Language with Networking: a dataflow language geared towards for example multimedia processing Back to:
Erlang Language: is a programming language for building robust fault-tolerant distributed applications Back to:
Multiple Instruction Multiple Data: machines have a number of processors that function asynchronously and independently Back to:
Multiple Instruction Single Data: fault tolerant system, or pipeline system Back to:
Message Passing Interface: specification used in a distributed memory model Back to:
Multiple Program Multiple Data: server farms are a good example of a MPMD system Back to:
Open Accelerators: extends OpenMP for support of accelerators Back to:
Open Computing Language: one of the most dominant libraries for parallel computing using task-based and data-based parallelism Back to:
Open Hybrid Multicore Parallel Programming: provides extensions for hardware accelerators. "What OpenMP is for multi-threaded programming" Back to:
Open Multi Processing: shared-memory parallel programming interface Back to:
Open Symmetric Hierarchical Memory access: parallel programming libraries Back to:
Parallel Architectures Library: library for coprocessors and parallel machines Back to:
Symmetric Hierarchical Memory access: parallel programming libraries Back to:
Single Instruction Multiple Data: one microinstruction can operate at the same time on multiple data items Back to:
Single Instruction Single Data: simple single core computer Back to:
Simple Linux Utility for Resource Management: a fault-tolerant, and highly scalable cluster management and job scheduling system Back to:
Single Program Multiple Data: multiple autonomous processors simultaneously execute the same program at independent points Back to:
CPU with instructions that act on one dimensional arrays of data (vectors) Back to:
Andreas Olofsson, Tomas Nordström, Zain Ul-Abdin "Kickstarting high-performance energy-efficient manycore architectures with Epiphany" 2014 48th Asilomar Conference on Signals, Systems and Computers Back to:
article: "The chips are down for Moore's law", Nature weekly journal of science, 2016 Back to:
"Some Computer Organizations and Their Effectiveness", IEEE Transactions on Computers. Vol. c-21, No.9, September 1972 Back to:
slides: "It's the end of the world as we know it ...", University of Bristol, HPC Research Group, 2015 Back to:
System Requirements
https://github.com/peteasa/parallella-yoctobuild/wiki
Testing the System
Single Instruction Single Data
What usually requires a loop of instructions can be performed in one instruction #pragma simd
for (i=0; i<n; i++){
a[i] = a[i] + b[i] + c[i] + d[i] + e[i];
}
Multiple Instruction Multiple Data
Multiple Program Multiple Data
A Programming Language#include "mpi.h"
..
MPI_Status Stat; // required variable for receive routines
MPI_Init(&argc,&argv);
MPI_Comm_size(MPI_COMM_WORLD, &numtasks);
MPI_Comm_rank(MPI_COMM_WORLD, &rank);
..
MPI_Send(&outmsg, 1, MPI_CHAR, dest, tag, MPI_COMM_WORLD);
MPI_Recv(&inmsg, 1, MPI_CHAR, source, tag, MPI_COMM_WORLD, &Stat);
// Create buffers on host and device
size_t size = 100000 * sizeof(int);
int* h_buffer = (int*)malloc(size);
cl_mem d_buffer = clCreateBuffer(context, CL_MEM_READ_WRITE, size, NULL, NULL);
...
// Write to buffer object from host memory
clEnqueueWriteBuffer(cmd_queue, d_buffer, CL_FALSE, 0, size, h_buffer, 0, NULL, NULL);
...
// Read from buffer object to host memory
clEnqueueReadBuffer(cmd_queue, d_buffer, CL_TRUE, 0, size, h_buffer, 0, NULL, NULL);
Example of the pragma used to create codelet:
#pragma hmpp simple1 codelet, args[outv].io=inout, target=CUDA
#pragma hmpp simple1 callsite, args[outv].size={n}
Parallel Architectures Library 2#include "pal_base.h"
...
team0 = p_open(dev0, 0, NUMBEROFCORES); // open a team
results_mem = p_map(dev0, MEM_RESULTS, SIZERESULTS);
err = p_run(prog0, func, team0, 0, NUMBEROFCORES, 0, NULL, 0);
size = p_read(&results_mem, results, 0, sizeof(*results) * NUMBEROFCORES, 0);
#include "pal_math.h"
...
rank = p_team_rank(P_TEAM_DEFAULT);
...
for (i = 0; i < n; i++)
out[i] = p_sqrt(ai[i]);
#include <omp.h>
...
#pragma omp parallel
#ifdef _OPENMP
printf("Hello, world from thread %d\n", omp_get_thread_num());
#else
printf("Hello, world!\n");
#endif