Streaming as a pattern. Peter Mattson, Richard Lethin Reservoir Labs

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Denis Russell
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1 Streaming as a pattern Peter Mattson, Richard Lethin Reservoir Labs

2 Streaming as a pattern Streaming is a pattern in efficient implementations of computation- and data-intensive applications Pattern has three key characteristics: Processing is largely parallel Data access patterns are apparent Control is high-level, steady, simple

3 Causes of the pattern Applications process, simulate, or render physical systems Architectural limits Application qualities Processing is largely parallel Data access patterns are apparent Independence of spatially and/or temporally distributed data Continuous, N- dimensional spatial/temporal data VLIW, SIMD, multiprocessor demand for parallelism Small local memories Streaming Architecture limits Control is high-level, steady, simple Few tasks per application, few unpredictable events Handling unpredictability in hardware is expensive

4 Streaming is not only 1D Multidimensional data, data rearrangement Multidimensional architecture topologies Time is favored dimension, yielding 1D tendency Unmapped, coarse-grain streams of whole-array elements moving between actors with whole-loopnest invocations Serialization for transmission, especially address stream to memory to get data stream to processor

5 Using streaming Streaming applications expose maximum opportunities, information to Streaming architectures expose maximum resources, control to the Streaming languages should enforce the pattern Force programmer thought to reach pattern Guarantee pattern to Streaming s should exploit the pattern Expand scope of application optimization Expand scope of resource choreography

6 Streaming languages Enforce similar, but not identical patterns StreamIt Think structured synchronous data flow Single stream graph Streams are infinite length Static rates Filters can have state, may require sequential processing Designed by people, clean but more constrained Brook/StreamC Think pointer-less C, with embedded dataflow graphs instead of loop nests Multiple stream graphs, surrounded by C-subset Streams are finite length Dynamic rates Kernels must be state-less, allow parallel processing Designed by application and architecture people, rough but more expressive Applications qualities Architecture limits

7 Streaming s Expand scope of optimization Application is transparent to Access patterns, aliasing, etc. completely known Top-down, not just bottom-up optimization Exploit task parallelism Expand scope of choreography Compiler lays out all computation, data, and communication Closely model architecture VLIW scheduling writ large Difficult! Many variables, phase ordering Whole program Multiple loop nests Single loop nest CSE etc. ALUs, registers Cache Local memory Multiprocessor, routing

8 R-Stream Reservoir Labs is developing the R-Stream high-level Goal is portable, consistently high-performance compilation High-level machine model Processor, memory, interprocessor communication mapping VLIW scheduling, precise routing Brook StreamIt R-Stream high-level Streaming Virtual Machine (SVM) code Architecturespecific low-level Binary executable

9 R-Stream Reservoir Labs is developing the R-Stream high-level Goal is portable, consistently high-performance compilation High-level machine model Streaming IR Top-down optimization Unified mapping of computation and data Brook StreamIt R-Stream high-level Streaming Virtual Machine (SVM) code Architecturespecific low-level Binary executable

10 Streaming Pattern Characterized by parallelism, apparent data access, high-level etc. control Driven by application class, architectural limits Enforced by languages, exploited by s Used by R-Stream; goal of portable, consistently high-performance compilation

DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING UNIT-1

DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING Year & Semester : III/VI Section : CSE-1 & CSE-2 Subject Code : CS2354 Subject Name : Advanced Computer Architecture Degree & Branch : B.E C.S.E. UNIT-1 1.