CSC/ECE 506 Spring 2012/ch2b cm

Introduction

This article provides an overview of data parallelism and how modern GPUs (specifically NVidia GeForce Family architecture) exploit it to achieve super computing capabilities. It provides a brief discussion of the commonly encountered problems that necessitate the application of parallel computing techniques. It illustrates some of the facilities made available by these architectures which should be enough to get you started with basic GPU programming.

Why GPUs

GPUs are not a replacement for CPUs. Both have their specific advantages for different kinds of software.

For Applications that require a limited number of threads with high data locality , a mix of operations and frequently occurring branch conditions, CPUs are better suited.

For Applications that have multiple threads and longer computational sequence of instructions, a GPU is better suited. Lately, GPUs are also becoming better equipped to handle CPU-like features. For Example, Data Caching,Virtual Memory Management , Flow Control etc.

Terminology

Parallel programming model

A parallel programming model is a concept that enables the expression of parallel programs which can be compiled and executed. The value of a programming model is usually judged on its generality: how well a range of different problems can be expressed and how well they execute on a range of different architectures. The implementation of a programming model can take several forms such as libraries invoked from traditional sequential languages, language extensions, or complete new execution models.

Data parallelism

Data parallelism refers to scenarios in which the same operation is performed concurrently (that is, in parallel) on elements in a source collection or array. Data parallelism with imperative syntax is supported by several overloads of the For and ForEach methods in the System.Threading.Tasks.Parallel class. In data parallel operations, the source collection is partitioned so that multiple threads can operate on different segments concurrently.

Embarrassingly Parallel problems

In parallel computing, an embarrassingly parallel workload (or embarrassingly parallel problem) is one for which little or no effort is required to separate the problem into a number of parallel tasks. This is often the case where there exists no dependency (or communication) between those parallel tasks.

Some Basics

Architecture overview

In this Section , we will discuss the Architecture of GeForce gtx 580 GPU from NVIDIA. The GTX 400/500 series GPU’s are based on NVIDIA’s Fermi Architecture, which has been the most significant leap in terms of Architecture design since the advent of the unified processor designs in the G80s.

Fermi's GPU Architecture consists of multiple Streaming Multiprocessors, each with its own 32 execution cores, as shown in the figure. The SMs are supported by L2 cache, host Interface, GigaThread Scheduler and multiple DRAM interfaces.

Streaming Multiprocessor

Fermi's third generation Streaming Multiprocessor, consists of 32 CUDA Processors that help improve the performance of graphics and compute processing. comprise 32 cores, each of which can perform floating-point and integer operations, along with 16 load-store units for memory operations, four special-function units, and 64K of local SRAM split between cache and local memory. The Multiprocessor introduces improved scheduling by using groups of 32 threads called warps. The SM consists of two warp schedules and two instruction dispatch units, allowing the two warps to execute simultaneously. The warps work independently of each other.

New Cache Architecture

Fermi provides a fully cached memory access with a unified cache architecture that supports graphics and compute programs. The Fermi Cache architecture as shown below has access to a texture cache[1], an L1 cache and an L2 cache. L1 and L2 caches help in improving the random memory access performance while the texture cache enables faster texture filtering.The programs also have access to a fast and dedicated Shared Memory.

Underneath the hood, the Nvidia GPU was designed to be data-centric, but for a graphics display the data are triangles and textures. The CUDA architecture layer makes the GPU look more processor-like, and more appropriate for general parallel problems.

C Runtime overview

This section aims to describe basic C programming model provided by NVidia CUDA architecture and to some extent the APIs provided for parallel programming.

Kernels

Kernels are similar to C functions, except that they are executed N times parallely on N CUDA threads instead of only once like regular C functions. Kernels are declared using __global__ specifier and the number of threads to execute is specified using the "execution configuration" <<<...>>> syntax. Each thread that executes within a kernel is given a unique identifier (obtained using threadIdx variable). Specific examples of using these abstractions follow in the next section.

Thread Hierarchy

Unlike threads in regular Operating systems, CUDA provides programmers to arrange the threads in 1 dimension, 2 dimension and 3 dimension structures called Blocks. Hence the threadIdx variable is actually a 3 component vector, i.e threadIdx.x, threadIdx.y, threadIdx.z may be used to access a particular thread (x,y,z) in the 3D block. This provides programmers a natural way to perform parallel computations in domains such as Arrays,Matrices and Volumes.

Blocks in turn can co exist with other blocks of threads, where a group of blocks is termed as Grid. Again blocks can be arranged in 1 dimension, 2 dimension and 3 dimension structures within a Grid and hence enable creation of large number of parallel threads that work in multiple dimensions of data. A thread may access the unique block Id in which it is running by using the variable blockIdx. Thread blocks are required to execute independently: It must be possible to execute them in any order, in parallel or in series. This independence requirement allows thread blocks to be scheduled in any order across any number of cores.

An important requirement of a parallel programming model is providing a way to synchronize between the threads. This is typically done by inserting synchronization barriers in the program. CUDA allows programmers to specify synchronization points in the Kernel using the light weight __synthreads() function.

Memory Hierarchy

As mentioned above, memory access latency is not a major concern for data parallel applications. But still a good architecture strives to provide fast, efficient memory access to thousands of threads that run in parallel. In CUDA, each thread has access to local memory and fast shared memory, visible to all threads within a block and with same lifetime as the block. All threads also have access to a global device memory which is somewhat slower. There are also two additional read-only memory spaces accessible by all threads: the constant and texture memory spaces, which are optimized for different memory usage patterns. The global, constant, and texture memory spaces are persistent across kernel launches by the same application.

Traditional Problem

Following is a most elementary problem that illustrates the essence of data parallelism. It involves computing the addition of two vectors and storing the result into a third vector. A standard C program to achieve this would look like this:

int main()
{
	float A[1000],B[1000],C[1000];
	.....	
	//Some initializations
	.....

	for(int i = 0; i < 1000; i++)
		C[i] = A[i] + B[i];

	......
}

Here we are executing the for loop, once for each array index. On a sequential processor (Single core processor),the instructions similar to the following are executed for each run of the for loop.

Fetch i
Fetch A[i] into Register EAX
Fetch B[i] into Register EBX
Add EBX to EAX
Store EAX into Adress of C[i]

Hence the time taken to perform this simple computation is linear in the size of the input. This could manifest as a major bottleneck in real time applications such as 3D renderings, Face recognition, Monte Carlo simulation,etc where the input to be processed is embarrassingly large.

Solution

The pattern that exists in all of these problems is that, there's one instruction or a single set of instructions that act on a large amount of distinct data elements (SIMD [2]). This is a typical pattern where data parallelism can be applied.

In a two processor (or two core) system, we could have both the processing units (cores) work on a part of the array simultaneously. Following snippet is a rough outline of how this could be achieved:

.....
    if CPU = "a"
       lower_limit := 0
       upper_limit := round(A.length/2)
    else if CPU = "b"
       lower_limit := round(A.length/2) + 1
       upper_limit := A.length

for i from lower_limit to upper_limit by 1
   C[i] = A[i] + B[i];
.....

This is typically done in high level programming languages such as C using the Thread[3] abstraction, where each thread runs simultaneously on a different processing unit. Even though this solution is much faster, it is still limited to just two parallel executions.

GPU solution

Because the same program (set of instructions) is executed for each data element, there is a lower requirement for sophisticated flow control, and because it is executed on many data elements, it exhibits high arithmetic intensity. GPU is specialized for compute-intensive, highly parallel computation – exactly what graphics rendering is about – and therefore designed such that more transistors are devoted to data processing rather than data caching and flow control. Hence GPU (more specifically CUDA) would be the best answer to such a problem.

Similar to what we did with the two processor, multi-threaded solution, CUDA enables us to create thousands of threads and run them parallely on thousands of Processing units that GPU provides. Following C program illustrates how this can be achieved:

__global__ void VecAdd(float* A, float* B, float* C)
{
	int i = threadIdx.x;
	C[i] = A[i] + B[i];
}

int main()
{
	...
	// Invocation with N threads
	VecAdd<<<1, 1000>>>(A, B, C);
	...
}

In this code, the main function simply calls a specialized routine (called Kernel [4]), and passes in the 3 arrays that need to be processed parallely. It also informs the CUDA runtime [5] to create 1000 threads, where each thread runs the same VecAdd routine, but on different data. As can be seen from the code, in the VecAdd routine, we are finding the thread index and acting only on that index of the input arrays. Hence each of the 1000 threads acts on a different location and performs the computation. This helps us achieve exponential performance improvements, which becomes evident when dealing with super-parallel applications such as those mentioned above.

Slightly Evolved Problem

Let us increase slightly the complexity of the above problem. Consider the program that adds two NxN matrices. A simple CPU program to achieve this may look like this:

void add_matrix( float* a, float* b, float* c, int N ) 
{ 
	int index; 
	for ( int i = 0; i < N; ++i ) 
	for ( int j = 0; j < N; ++j ) 
	{ 
		index = i + j*N; 
		c[index] = a[index] + b[index]; 
	} 
} 
 
int main() 
{
    add_matrix( a, b, c, N );
}

As discused before, CUDA provides a hierarchy of threads, which can be exploted to achieve fast parallel computation in 2D and 3D as well. In order to achieve data parallelism in 2D or 3D, we can use the 3 components (x,y,z) of threadIdx variable.For a two-dimensional block of size (Dx, Dy), the thread ID of a thread of index (x, y) is (x + y Dx); for a three-dimensional block of size (Dx, Dy, Dz), the thread ID of a thread of index (x, y, z) is (x + y Dx + z Dx Dy). Based on this idea, the following code adds two NxN matrices in parallel:

__global__ void MatAdd(float A[N][N], float B[N][N],float C[N][N])
{
	int i = threadIdx.x;
	int j = threadIdx.y;
	C[i][j] = A[i][j] + B[i][j];
}
int main()
{
	...
	// Invocation with one block of N * N * 1 threads
	int numBlocks = 1;
	dim3 threadsPerBlock(N, N);
	MatAdd<<<numBlocks, threadsPerBlock>>>(A, B, C);
	...
}

MatAdd kernel here performs the N*N additions almost at the same time, assuming the GPU has so many processing entities. Hence the performance boost obtained is easily exponential as compared to regular CPU computation.

Some interesting Applications

• IBM Watson

IBM Watson[6] has established itself as one of the fastest super computers by competing against best human players in the game of Jeopardy. If executed on a single core, it takes it approximately 2 hours to answer a single Jeopardy question. But the "real IBM Watson" with 2880 cores performs the same task in less than 3 seconds.

• Password guessing [7]

Technological attacks involve using computers to guess passwords. This is typically accomplished by having the computer try every possible password until it finds the correct one. Advances in GPUs have enabled brute force attacks to be executed faster than ever, hence making this a major security challenge.

• Air traffic control

Rapid prediction of aircraft trajectories is critical for decision making in future Trajectory Based Operations. GPU computing allows you to exploit the parallelism in the trajectory prediction process to have extremely fast run-times. This in turn allows you to achieve real-time performance and analyze models with greater complexity.

References

Parallel programming model

Data Parallelism

Embarrassingly Parallel Problems

Questions

1.What advantage does CPU have over GPU?

2.Name the Latest GPU whose Architecture is discussed in this article.

3.How many CUDA Processors are present in the NVIDIA Fermi Architecture?

4.How are kernels in CUDA Programming different from normal C functions?

5.What are the different memory spaces provided by the CUDA Architecture?

6.How does SIMD work?

7.What is an Embarrassingly Parallel Problem?

8.Can a thread block be three dimensional?

9.If Yes, How do you access a specific thread in a block?

10.Name one interesting research happening in Parallel Computing.