CS 395 Last Lecture

Summary, Anti-summary, and Final Thoughts

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Summary (1) Architecture

• Modern architecture designs are driven by energy constraints

• Shortening latencies is too costly, so we use parallelism in hardware to increase potential throughput

• Some parallelism is implicit (out-of-order superscalar processing,) but have limits

• Others are explicit (vectorization and multithreading,) and rely on software to unlock

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Summary (2) Memory

• Memory technologies trade off energy and cost for capacity, with SRAM registers on one end and spinning platter hard disks on the other

• Locality (relationships between memory accesses) can help us get the best of all cases

• Caching is the hardware-only solution to capturing locality, but software-driven solutions exist too (memcache for files, etc.)

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Summary (3) Software

• Want to fully occupy your hardware?– Express locality (tiling)– Vectorize (compiler or manual)– Multithread (e.g. OpenMP)– Accelerate (e.g. CUDA, OpenCL)

• Take the cost into consideration. Unless you’re optimizing in your free time, your time isn’t free.

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Research Perspective (2010)

• Can we generalize and categorize the most important, generally applicable GPU Computing software optimizations?– Across multiple architectures– Across many applications

• What kinds of performance trends are we seeing from successive GPU generations?

• Conclusion – GPUs aren’t special, and parallel programming is getting easier

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Application Survey

• Surveyed the GPU Computing Gems chapters• Studied the Parboil benchmarks in detail

Results: • Eight (for now) major categories of

optimization transformations– Performance impact of individual optimizations on

certain Parboil benchmarks included in the paper

1: (Input) Data Access Tiling

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DRAM

DRAM

Cache

DRAM

Scratchpad

ExplicitCopy

ImplicitCopy

LocalAccess

LocalAccess

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2. (Output) Privatization

• Avoid contention by aggregating updates locally

• Requires storage resources to keep copies of data structures

PrivateResults

LocalResults

GlobalResults

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Running Example: SpMVAx = v

Row

Data

Col

vx

A

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Running Example: SpMVAx = v

Row

Data

Col

A

vx

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3. “Scatter to Gather” Transformation

Ax = v v

Row

Data

Col

A

x

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3. “Scatter to Gather” Transformation

Ax = v v

Row

Data

Col

A

x

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4. Binning

A

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5. Regularization (Load Balancing)

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6. Compaction

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7. Data Layout Transformation

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7. Data Layout Transformation

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8. Granularity Coarsening• Parallel execution often requires redundant and

coordination work– Merging multiple threads into one allows reuse of result,

reducing redundancy

Essential

Redundant

4-wayparallel

2-wayparallel

Time

How much faster do applications really get each hardware generation?

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Unoptimized Code Has Improved Drastically

• Orders of magnitude speedup in many cases

• Hardware does not solve all problems– Coalescing (lbm)– Highly contentious

atomics (bfs)

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Optimized Code Is Improving Faster than “Peak Performance”

• Caches capture locality scratchpad can’t efficiently (spmv, stencil)

• Increased local storage capacity enables extra optimization (sad)

• Some benchmarks need atomic throughput more than flops (bfs, histo)

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Optimization Still Matters• Hardware never

changes algorithmic complexity (cutcp)

• Caches do not solve layout problems for big data (lbm)

• Coarsening still makes a big difference (cutcp, sgemm)

• Many artificial performance cliffs are gone (sgemm, tpacf, mri-q)

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Stuff we haven’t covered

• Good tools out there for profiling code beyond good timing (cache misses, etc.) If you can’t find why a particular piece of code is taking so long, look into hardware performance counters.

• Patterns and practice– Some of the major patterns of optimization we

covered, but only the basic ones. Many optimization patterns are algorithmic.

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