Debunking the 100X GPU vs. CPU Myth: An Evaluation of Throughput

Computing on CPU and GPUPresented by: Ahmad Lashgar

ECE Department, University of TehranSeminar of Parallel Processing. Instructor: Dr. Fakhraie

29 Dec 11

ISCA 2010Original authors: Victor W Lee et al.

Intel Corporation

1Some slides are included from original paper only for educational purposes

Abstract

• Is the GPU silver bullet of parallel computing?• How far is the difference between peak and

achievable performance?

2

Overview

• Abstract• Architecture

– CPU: Intel core i7– GPU: Nvidia GTX280

• Implications for throughput computing applications• Methodology• Results• Analyzing the results• Platform optimization guides• Conclusion

3

Architecture (1)

• Intel core i7-960– 4-core, 3.2 GHz– 2-way multi-threading– 4-wide– L1 32KB, L2 256KB, L3 3MB– 32 GB/sec

4[DIXON’2010]

Architecture (2)

• Nvidia GTX280– 30 core, 1.3GHz– 1024-way multi-threading– 8-way SIMD– 16KB software managed cache (shared

memory)– 141 GB/s

5[LINDHOLM’2008]

Architecture (3)

Core i7-960 GTX280

Core 4 30

Frequency (GHz) 3.2 1.3

Transistors 0.7B (263mm2) 1.4B (576mm2)

Memory Bandwidth (GB/s) 32 141

SP SIMD 4 8

DP SIMD 2 1

Peak SP scalar GFLOPS 25.6 116.6

Peak SP SIMD GFLOPS 102.4 311.1 (933.1)

Peak DB SIMD GFLOPS 51.2 77.8

Red texts are not the author’s numbers.6

Implications for throughput computing applications

1. Number of core difference2. Cache size/multi-threading3. Bandwidth difference

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1. Number of cores difference

• It is all about the core complexity:– The common goal: Improving pipeline efficiency– CPU goal: Single-thread performance

• Exploiting ILP• Sophisticated branch predictor• Multiple issue logics

– GPU goal: Throughput• Interleaving hundreds of threads

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2. Cache size/multi-threading

• CPU goal: reducing memory latency– Programmer-transparent data caching

• Increasing the cache size to capture the working set– Prefetching (HW/SW)

• GPU goal: hiding memory latency– Interleave the execution of hundreds of threads to hide

the latency of each other• Notice:– CPU uses multi-threading for latency hiding– GPU uses software controlled caching (shared memory)

for reducing memory latency

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3. Bandwidth difference

• Bandwidth versus latency• CPU goal: single thread performance– Workloads do not demand for many memory accesses– Bring the data as soon as possible

• GPU goal: throughput– There are lots of memory accesses, provide the good

bandwidth– No matter the latency, core will hide it!

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Methodology (1)

• Hardware– Intel Core i7-960, 6GB DRAM, GTX280 1GB

• Software– SUSE Enterprise 11– CUDA Toolkit 2.3

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Methodology (2)

• Optimizations– On CPU:

• SGEMM, SpMV and FFT from Intel MKL 10• Always 2 threads per core

– On GPU:• Best possible algorithm for SpMV, FFT and MC• Often 128 to 256 threads per core (to leverage shared memory

and register-file usage)

– Interleaving GPU execution and HD/DH memory transfers where possible

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Results

• The HD/DH data transfer time is not considered• Only 2.5X on average– Far from what is reported by previous researches (100X)

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Where is the speedup of previous researches?!

• What CPU and GPU are compared?• How much optimization is performed on CPU and

GPU?– Where they optimize both platforms, they reported much

lower speedup (like this paper)

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Analyzing the results (1)

1. Bandwidth2. Compute flops (single precision)3. Compute flops (double precision)4. Reduction and synchronization5. Fixed function

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Analyzing the results (2)

1. Bandwidth– Peak: GTX280/Corei7-960 ~ 4.7X– Feature: Large working set, Performance is bounded by

the bandwidth– Examples

• SAXPY (5.3X)• LBM (5X)• SpMV (1.9X)

– CPU benefits from caching

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Analyzing the results (3)

2. Compute Flops (Single Precision)– Peak: GTX280/Corei7-960 ~ 3X– Feature: Bounded by computation, benefit from more

cores– Examples

• SGEMM, Conv and FFT (2.8-4X)

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Analyzing the results (4)

3. Compute Flops (Double Precision)– Peak: GTX280/Corei7-960 ~ 1.5X– Feature: Bounded by computation, benefit from more

cores– Examples

• MC (1.8X)• Blitz (5X)

– Uses transcendental operations

• Sort (1.25X slower)– Due to decrease in SIMD width usage– Depends on scalar performance

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Analyzing the results (5)

4. Reduction and Synchronization– Feature: More threads, higher the synchronization

overhead– Examples

• Hist (1.8X)– On CPU, 28% of the time is spent on atomic operations– On GPU, the atomic operations are much slower

• Solv (1.9X slower)– Multiple kernel launches to preserve cache coherency on GPU

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Analyzing the results (6)

5. Fixed function– Feature: Interpolation, texturing and transcendental

operation are bonus on GPU– Examples

• Bilat (5.7X)– On CPU, 66% of the time is spent on transcendental operations

• GJK (14.9X)– Uses texture lookup

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Platform optimization guides

• CPU programmer have heavily relied on increasing clock frequency

• Their application do not benefits from TLP and DLP• Today CPUs use wider SIMD which stays idle if not

exploited by programmer (or compiler)• This paper showed that careful multi-threading can

reduce the gap heavily– For LBM, from 114X down to 5X

• Let’s learn some optimization tips from the authors

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CPU optimization

• Scalability (4X):– Scale the kernel with the number of threads

• Blocking (5X):– Be aware of cache hierarchy and use it efficiently

• Regularizing (1.5X):– Align the data regularly to take advantage of SIMD

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GPU optimization

• Global synchronization– Reduce the atomic operations

• Shared memory– Use shared memory to reduce of-chip demand– Shared memory is multi-banked and is efficient for

gathers/scatters operations

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Conclusion

• This work analyzed the performance of important throughput computing kernels on CPU and GPU– the gap is much lower that previous reports (~2.5X)

• Recommendation for a throughput computing architecture:– High compute– High bandwidth– Large cache– Gather/scatter support– Efficient synchronization– Fixed function units

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Thank you for your attention.any question?

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References

[LEE’2010] V. W. Lee et al, Debunking the 100X GPU vs. CPU Myth: An Evaluation of Throughput Computing on CPU and GPU, ISCA 2010

[DIXON’2010] M. Dixon et al, The next-generation Intel® Core ™ Microarchitecture, Intel® Technology Journal, Volume 14 Issue 3, 2010

[LINDHOLM’2008] E. Lindholm et al, NVIDIA Tesla A Unified Graphics and Computing Architecture, IEEE Micro 2008

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