STREAM COMPUTING A SEMINAR REPORT
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Computer Science Clay
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14-06-2009, 09:28 AM


A SEMINAR REPORT ON
STREAM COMPUTING

Submitted by:AMIT KUMAR
COMPUTER SCIENCE & ENGINEERING
SCHOOL OF ENGINEERING
COCHIN UNIVERSITY OF SCIENCE AND
TECHNOLOGY,
COCHIN 682022
AUGUST 2008
Abstract
Stream computing is a programming paradigm that models a com- puter program as a stream of data between several processing units, rather than as an implemented algorithm processing data. The prin- ciple originates from the needs of real time multimedia applications. These applications can be divided into multiple data streams - e.g. au- dio and video streams - that must be delivered to the data consumer in a just-in-time manner. In this paper, we explore the problems and opportunities that this new paradigm can bring to the field of classical high performance computing (HPC). Stream computing is well suited for application on non-classical types hardware, such as asymmetric multiprocessors or graphical processing units (GPU). We show how classic HPC can be mapped on non-classic hardware. This mapping alters the imple- mented algorithms, but also incluses information of the specific prob- lem that is solved. Solutions are less generic than in a normal imple- mentation or library. As an illustration, we present a streaming frame- work on the Cell processor that shows the obstructions that must be overcome.

The main task is to pull in streams of data, process the data and stream it back out as a single flow and thereby analyzes multiple data streams from many sources live. Stream computing uses software algorithms that analyzes the data in real time as it streams in to increase speed and accuracy when dealing with data handling and analysis. System S, the stream computing system of IBM, introduced in June 2007, runs on 800 microprocessors and the System S software enables software applications to split up tasks and then reassemble the data into an answer. ATI Technologies also announced a stream computing technology derived from a class of applications that run on the GPU instead of a CPU which enables the graphics processors (GPUs) to work in conjunction with high-performance, low-latency CPUs to solve complex computational problems.

INTRODUCTION
What is stream computing exactly? As a beginning here is a definition Stream computing is a programming paradigm that models a computer program as a stream of data between several processing units,rather than as an implemented algorithm processing data Like other definitions of topics like these, an understanding of the term stream computing requires an understanding of various other terms which are closely related to this. While there is a lack of precise scientific definitions for many of these terms, general definitions can be given. Computing can be described as any activity of using and/or developing computer hardware and software. It includes everything that sits in the bottom layer, i.e. everything from raw compute power to storage capabilities. Stream processing is a computer programming paradigm, related to SIMD that allows some applications to more easily exploit a limited form of parallel processing. Such applications can use multiple computational units, such as the floating poinits on a GPU without explicitly managing allocation, synchronization, or communication among those units. The stream processing paradigm simplifies parallel software and hardware by restricting the parallel computation that can be performed. Given a set of data (a stream), a series of operations (kernel functions) are applied to each element in the stream. Uniform streaming, where one kernel function is applied to all elements in the stream, is typical. Kernel functions are usually pipelined, and local on-chip memory is reused to minimize external memory bandwidth. Since the kernel and stream abstractions expose data dependencies, compiler tools can fully automate and optimize on-chip management tasks. Stream processing hardware can use score boarding, for example, to launch DMAs at runtime, when dependencies become known. The elimination of manual DMA management reduces software complexity, and the elimination of hardware caches reduces the amount of die area not dedicated to computational units such as ALUs.
2. Stream computing is a programming paradigm that models a computer program as a stream of data between several processing units, rather than as an implemented algorithm processing data. StreamIt is a programming language and a compilation infrastructure, specifically engineered for modern streaming systems. It is designed to facilitate the programming of large streaming applications, as well as their efficient and effective mapping to a wide variety of target architectures, including commercial-off-the-shelf uniprocessors , multicore architectures, and clusters of workstations. I.B.M.is introducing a high-performance computer system that is intended to rapidly analyze data as it streams in from many sources, increasing the speed and accuracy of decision making in fields as diverse as security surveillance and Wall Street trading.The company plans to demonstrate the system, called System S, at a conference of Wall Street technology managers today. The announcement, analysts say, is a significant step in the commercialization of the emerging technology of stream computing. Early this month Google acquired Peak Stream, a start-up in stream computing, and industry analysts say its software could help Google improve its video search functions. Stream computing is an effort to deal with two issues: the need for faster data handling and analysis in business and science, and the growing flood of information in digital form, including Web sites, blogs, e-mail, video and news clips, telephone conversations, transaction data and electronic sensors. In stream computing, advanced software algorithms analyze the data as it streams in. Text, voice and image-recognition technology, for example, can be used to determine that some data is more relevant to a particular problem than others. The priority data is then shuttled off into a program tailored to work on complex, fast-changing problems like tracking an epidemic and predicting its spread, or culling data from electronic sensors in a computer chip plant to quickly correct flaws in manufacturing. I.B.M. deems its System S research project and implimentation ready to make its way into the marketplace. The planned announcement to the Wall Street group is the beginning of its effort to find industry partners .The initial system runs on about 800 microprocessors, though it can scale up to tens of thousands as needed, I.B.M. said. The most notable step, researchers say, lies in the System S software, which enables software applications to split up tasks like image recognition and text recognition, and then reassemble the pieces of the puzzle into an answer.
2.1 Characteristics of Enable new applications on new architectures
1. Parallel problems other than graphics that map well on GPU architecture .
2. Transition from fixed function to programmable pipelines.
3. Various proof points in research and industry under the name GPGPU .
4. Data dependencies and parallelism.
5. A great advantage of the stream programming model lies in the kernel
defining independent and local data usage. Kernel operations define the basic data unit, both as input and output. This allows the hardware to better allocate resources and schedule global I/O. Although usually not exposed in the programming model, the I/O operations seems to be much more advanced on stream processors (at least, on GPUs). I/O operations are also usually pipelined by themselves while chip structure can help hide latencies. Definition of the data unit is usually explicit in the kernel, which is expected to have well-defined inputs (possibly using structures, which is encouraged) and outputs. In some environments, output values are fixed (in GPUs for example, there is a fixed set of output attributes, unless this is relaxed). Having each computing block clearly independent and defined allows to schedule bulk read or write operations, greatly increasing cache and memory bus efficiency.

3. Need for How does stream computing differ from computation on the CPU?
Stream computing takes advantage of a SIMD methodology (single instruction, multiple data) whereas a CPU is a modified SISD methodology (single instruction, single data); modifications taking various parallelism techniques into account. Where by tens to hundreds of parallel operations are performed with each clock cycle whereas the CPU The benefit of stream computing stems from the highly parallel architecture of the GPU can at best work only a small handful of parallel operationsperclockcycle AMD's Fire Stream„¢ 9170, What are AMD stream computing product
Features
1. our latest generation stream computing GPU, features:
2. 320 stream cores (compute units or ALUs)
3. 2GB on-board GDDR3 memory
4. Double precision floating point support
5. PCIe 2.0 x16 interface
What are AMDâ„¢s stream computing product advantages? AMD's FireStream
9170
hardware:
1. Only company positioned to offer a unique platform with strengths in accelerated GPU as Stream Computing
2. Stream computing today leading to fusion tomorrow AMD's open systems SDK approach:
3. CTM initiative ” Release low level specifications to enable developers and wnd user to understand the architecture and tuning to maximize performance
4. Deliver high level, multi-targeted compilers through Brook, 3rd parties like rapid mind, and partnerships with universities and industry. Is stream computing return to the old coprocessor days? In many ways stream computing does resemble the days when vector co-processors handled substantial mathematical tasks. The benefit then as now is the remarkable performance boost gained through implementing these specialized components.
4. Enabling technologies
4.1 Stream Processor architecture
Stream processors are programmable processors that are optimized for executing programs expressed using the stream programming model. A block diagram of a stream processor The stream processor operates as a coprocessor under the control of the host processor, which is often a standard general-purpose CPU. A stream program executing on the host processor orchestrates the sequence of kernels to be executed and then necessary transfer of input and output data streams between the stream processor and o -chip memory. Kernel execution takes place directly on the stream processor from instructions stored in the microcontroller. New kernels may be loaded into them microcontroller as needed, possibly under explicit control of the host processor. The sequence of operations initiated by the host processor to orchestrate the stream program .Dependencies between these host-issued operations . The host interface of the stream processor issues the commands received from the host to the appropriate units as resources become available, subject to dependencies among the commands. Arithmetic units of the stream processor are grouped in to n identical compute clusters. Each cluster consists of several functional units and associated registers. A block diagram of an example cluster organization. The local register files (LRFs) attached to each functional unit provide the input operands for that unit, and results are written to one or more of the LRFs via the intra cluster network . Loop-carried state and other shared data may be communicated among Block diagram of stream processor architecture 4.2Processing stages in Stream Processing systems "A model that uses sequences of data and computation kernels to expose and exploit concurrency and locality for efficient. When using such a board for stream processing, a common system model is to distribute data out from the FPGA to other processors in the multicomputer, either in a round-robin or a next-avail- able-processor fashion FPGA toolkits provide drivers and a software library for managing these complex data movement strategies as well as interfaces for a wide range of board-related features, including node configuration , temperature and current sensors and control bus access. They also provide elements such as IP block libraries, simulation environments, BSPs, algorithm libraries and middleware. Processing stages in Stream Processing systems

4.3 StreamIt Language Overview
StreamIt is an architecture-independent language for streaming applications. It adopts the Cyclo-Static Dataflow [1] model of computation which is a generalization of Synchronous Dataflow . StreamIt programs are represented as graphs where nodes represent computation and edges represent FIFO-ordered communication of data over tapes. The basic programmable unit in StreamIt is a filter.Each filter contains a work function that executes atomically, popping (i.e., reading) a fixed number of item from the filters input tape and pushing (i.e., writing) a fixed number of items to the filters output tape. A filter may also peek at a given index on its input tape without consuming the item; this makes it simple to rep-resent computation over a sliding-window. The push,pop, and peek rates are declared as part of the work function, thereby enabling the compiler to construct a static schedule of filter firings StreamIt provides three hierarchical structures for composing filters into larger stream graphs .
4.4 StreamIt language Constructs
Programming paradigm is modular. Important for large scale development .Parametrized templates allows program to change behavior with small source code modifications ,it shows the malleability property. Composition of simple structures create and large graphs.It enables inductive reasoning about correctness. Application is architecture independent.
4.5 Filter as computational elements
Filters are the programmable units. An initialization function and a steady sate work fuction.communicate via FIFOâ„¢s:pop(),peek(index),push(value) float?float filter FIR (int N) {
float[N] weights;
init {
weights = calculate_weights(N
}
work push 1 pop 1 peek N {
float result = 0;
for (int i = 0; i < N; i++) {
result += weights[i] * peek(i);
}
push(result);
pop();}}
The filter can now serve as a module that is incorporated into stream graphs as necessary for example as part of an acoustic beam former. A filter is akin to a classin object oriented programming with the work function serving as the main method. A filter may also declare a constructor function to initialize the filter state before any other method is invoked. The implementation of the work function in StreamIt obviates the need for explicit buffer management. The application developer instead focuses on the hierarchical assembly of the stream graph and its communication topology.
4.6 Applications
Stream processing is essentially a compromise, driven by a data-centric model that works very well for traditional DSP or GPU-type applications (such as image, video and digital signal processing) but less so for general purpose processing with more randomized data access (such as databases). By sacrificing some flexibility in the model, the implications allow easier, faster and more efficient execution. Depending on the context, processor design may be tuned for maximum efficiency or a trade-off for flexibility. Stream processing is especially suitable for applications that exhibit three application characteristics .
Compute Intensity the number of arithmetic operations
per I/O or global memory reference. In many signal processing applications today it is well over 50:1 and increasing with algorithmic complexity .Data Parallelism exists in a kernel if the same function is applied to all records of an input stream and a number of records can be processed simultaneously without waiting for results from previous records. Data Locality is a specific type of temporal locality common in signal and media processing applications where data is produced once, read once or twice later in the application , and never read again. Intermediate streams passed between kernels as well as intermediate data within kernel functions can capture this locality directly using the stream processing programming model.

5. StreamIt Compiler
StreamIt compiler hides granularity of execution and architecture details. compiler backend supports uniprocessor ,cluster of workstations and MIT Raw. Innovative compiler Technology focuses on the core set of challenges to deliver high performance in future architectures .Automating domain specific Optimizations by optimizing of linear streams and translation of the frequency domain .It helps in partitioning, routing etc.
5.1 Linear Filter optimizations
StreamIt provides three hierarchical structures for composing filters into larger stream graphs (see Figure 1). The pipeline construct composes streams in sequence, with the output of one connected to the input of the next. The split join construct distributes data to a set of parallel streams, which are then joined together in a round robin fashion. The feedback loop provides a mechanism for introducing cycles in the graph. An ex-ample of a pipeline appears in Figure 2. It contains as ingle FIR (Finite Impulse Response) filter which could be implemented as follows.
5.2 Extracting Linear Representation
Resembles constant propagation Maintains linear form v, b for each variable Peek expression: generate fresh v Push expression: copy v into A Pop expression: increment
5.3 Combining Linear Filters
Pipelines and split joins can be collapsed .for example pipeline. Given below figure describes about the Combinational example
COMBINATION EXAMPLE
5.4 Linear optimization of Stream graph
5.5 Backend for parallel platforms
StreamIt exposes communication pattern Automatic generation and optimization of routing code Otherwise, may require extensive (assembly) programming FIR “ Raw backend. 15 statements of StreamIt code achieve the same performance as 352 statements of manually-tuned C. Frequency Hopping Radio-cluster backend 50% higher throughput and 35% less communication, when using StreamIt™ messaging construct
Development Support
6.1 StreamIt Development tool
The StreamIt Development Tool (SDT) features many aspects of an IDE, including a text editor and a debugger. For example, the SDT debugger supports line. and method breakpoints, watchpoints, program suspen-sion, code stepping, variable inspection and value modification to list a few.Moreover, the SDT offers features tailored to the StreamIt language. The SDT graphically represents StreamIt programs, and preserves hierarchical information to allow an application engineer to focus on the parts of the stream program that are of interest. In addition, the SDT can track the flow of data between filters and most importantly, it provides a deterministic mechanism to debug parallel streams.The SDT is implemented in Java as an Eclipse [3]plug-in. The Eclipse universal tools platform is an ex-tensible development environment. We leverage the built-in user interfaces for editing and viewing files, the resource management system, the documentation infrastructure, and the runtime support of launching, runningand debugging programs. 6.2 Debugging Parallel StreamIt programs
Parallelism and communication are exposed. Tracking the flow of data in a stream graph affords a frame of reference for reasoning about time. Powerful advantage when debugging parallel programs versus Multiple threads with independent program counters Non-deterministic execution
6.3 StreamIt graphical editor
We are making available the first public version of the StreamIt Development Tool (SDT). It is implemented in Java as an Eclipse plug in ,and intended for developing, debugging, and visualizing programs written in StreamIt. As a graphical programming environment, it can simply and intuitively convey the hierarchical and structured nature of a StreamIt application. In addition, the debugger can interpret and visually represent the stream graph and its dynamic behavior, including the flow of information in parallel stream graphs. The SDT is composed of the following modules: an IDE-integrated debugger, a graphical text editor, a runtime stream graph view, and a corresponding graph overview . 6.4 StreamIt Debugging Environment
As seen in Figure , a StreamIt program can be visually depicted as a hierarchical directed graph of streams, with graph nodes representing streams and graph edges representing tapes or channels. The containers are rendered according to the code declarations, and the visualization tools in the SDT allow the user to selectively collapse and expand containers. This is useful in large streams where the application developers are only interested in visualizing a particular subset, for example to verify the interconnect topology of the graph. In Figure , we show a screen shot of the SDT for a simple StreamIt program which consists of a filter that generates input data (Int Source), a split join (Echo) that operates on the data produced by the source and whose data is in turn consumed by an Adder. Lastly, a filter (IntPrinter) reads and prints the computed values
7. Conclusion
Stream processing has been shown to outperform mainstream programmable computing solutions while consuming less power for data parallel applications. Exploiting the data- and instruction-level parallelism inherent in these applications, stream processors sustain many operations in parallel, and overlap them with memory accesses in order to improve computation throughput. Realizing the performance potential of stream processing, however, depends on the ability to manage bandwidth demands in the memory hierarchy to sustain the operands needed for highly parallel computation. We introduced an indexed stream register file architecture that enabled data reuse patterns found in a broad range of data parallel applications to be captured in on chip memories of stream processors, reducing o chip bandwidth demands by several fold in some cases. This, in e ect , enables classes of data parallel applications that ,due to bandwidth bottlenecks, could not previously be e ciently executed on stream processors to be supported e ciently .
8. References
1 . cag.csail.mit.edu/streamitStreamIt Home
2.csm.ornl.gov/workshops/SOS11/presentations/d_rich.pdf
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26-11-2010, 10:32 AM


.ppt   Stream Computing.ppt (Size: 665 KB / Downloads: 128)
Presented By:Anil Kumar
Stream Computing



What is Stream Computing?
Stream Computing based on parallel processing, generally used for 2D or 3D graphics applications, to solve real-world problems.
Stream computing uses Software Algorithms that analyzes the data in real time.
It increase speed and accuracy when dealing with data handling and analysis.
A model that uses sequences of data and computation kernels to increase efficiency of concurrency.


Evolution of Stream Computing
In June 2007, IBM announced its stream computing system, called System S.

System S
Runs on 800 Microprocessor.
Enables Application to split up tasks and then reassemble the data.


ATI Technologies

Describes technology that enables the graphics processors (GPUs).
Work with high-performance.
Solve complex computational problems.
Applications that run on the GPU instead of a CPU.

Stream Processing
It is a computer programming related to SIMD (Single Instruction Multiple Data).

In Stream processing, for a given a set of data (a stream), a series of operations (kernel functions) are applied to each element in the stream.

Where we require Stream Processing?
Stream processing is especially suitable for applications that exhibit three application characteristics.

Compute Intensity:
The Large number of arithmetic operations per I/O.
More algorithmic complexity in process.


Data Parallelism :
Exists in a kernel and the same function is applied to all records of an input stream.
Number of records can be processed simultaneously without waiting for results from previous records.

Data Locality :
locality is common.
Intermediate data within kernel functions.


Why Stream Processing?
Stream Processing enables…
High message/data rates,
Low (msec-secs) latency,
Advanced analysis


Its Applications in :-
Embedded systems
Cell phones, handheld computers.
Desktop workstations
Streaming media, real-time encryption.
High-performance servers
Radar tracking, HDTV editing consoles.


Limitations

Enable GPU processor cores to communicate, synchronize, and share data.
Other Hardware device required for stream computing.








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projectsofme
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27-11-2010, 01:02 PM


.pptx   streamit.pptx (Size: 690.76 KB / Downloads: 82)
PRESENTED BY:
BHAGYALAXMI NAIK
STREAM COMPUTING



CONTENTS:
DEFINITION
CHARACTERISTICS
WHY IT IS NEEDED?
STREAM PROCESSOR ARCHITECTURE
STREAMIT LANGUAGE
STREAMIT COMPILER
DEVELOPMENT TOOLS
APPLICATION
CONCLUSION





DEFINITION:
“Stream computing is a programming paradigm that models a computer program as a stream of data between several processing units,rather than as an implemented algorithm processing data”

CHARACTERISTICS:
Parallel problems other than graphics that map well on GPU architecture .

Transition from fixed function to programmable pipelines.

Various proof points in research and industry under the name GPGPU .

Data dependencies and parallelism

NEED OF STREAM COMPUTING:
To support various parallelism techniques

For support of SIMD technology

Vector processing

Faster data handling

STREAMIt LANGUAGE:
Architecture independent language

Used for streaming applications

Adopts cyclo-static dataflow of computation


filter
It is the basic unit of computation
communicate with neighboring blocks using typed FIFO channels
The channels support three operations:
pop(): remove item from end of input channel
peek(i): get value i spaces from end of input channel
push(val): push value onto output channel 
Each filter contains: 
init(…) function
work() function


Continued…

splitter redistributes data to input channels of the child streams, and linking its output channel to a joiner.

joiner recombines data from the children’s output channels.

STREAMIt COMPILER:

StreamIt compiler hides granularity of execution and architecture details.


It is designed to facilitate the programming of large streaming applications.

STREAMIt DEVELOPMENT TOOLS:
SDT is composed of the following modules: an IDE-integrated debugger, a graphical text editor, a runtime stream graph view, and a corresponding graph overview.

graphically represents StreamIt programs, and preserves hierarchical information to allow an application engineer to focus on the parts of the stream program that are of interest.

CONTINUED…

SDT debugger supports line. and method breakpoints, watchpoints, program suspension, code stepping, variable inspection and value modification.

can track the flow of data between filters and it provides a deterministic mechanism to debug parallel streams.


APPLICATIONS:
Useful in DSP or GPU-type applications (such as image,video and digital signal processing) .

Compute intensity

CONCLUSION:
Stream processing has been shown to outperform mainstream programmable computing solutions while consuming less power for data parallel applications.
Exploiting the data- and instruction-level parallelism inherent in these applications,stream processors sustain many operations in parallel, and overlap them with memory accesses in order to improve computation throughput.
Realizing the performance potential of stream processing, however, depends on the ability to manage bandwidth demands in the memory hierarchy to sustain the operands needed for highly parallel computation.








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