InAccel provides high performance accelerators for your application based on novel hardware reconfigurable engines as IP blocks. The hardware accelerators can be deployed in the cloud, like Amazon AWS, using the f1 accelerators and can be integrated to widely used frameworks like Spark and PostgreSQL. The main novelty is that the users do not need to change the original code as the accelerators are deployed as software packages. In this talk we will show how machine learning applications (e.g. logistic regression and K-means), based on Spark, can be accelerated by 3x-10x using hardware accelerators that are deployed in the Amazon AWS using f1 accelerators without any changes on the Spark code. InAccel provides all the required APIs for the integration on Spark using Java, Python or Scala. The utilization of hardware accelerators can also be used to reduce the OpEx as less resources and less time is required for the processing of the data.

1. Chris Kachris www.inaccel.com
CEO, co-founder
chris@inaccel.com
Apache Spark
Acceleration using
FPGAs in the Cloud,
Seamlessly
#HWCSAIS16

2. …or
How to speedup your Spark
applications
with the same cost
with the same code
#HWCSAIS16

3. Why acceleration?
3www.inaccel.com, Chris Kachris, #HWCSAIS16
Source: APACHE SPARK SURVEY 2016 REPORT

4. The new era of heterogenous cloud
• DCs have started deploying Heterogenous
systems (GPGPUs, FPGAs) to face the
increased traffic requirements.
• A new emerging era has started: specialized
systems for big data applications and data
analytics
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5. FPGAs in the news
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6. Available Platforms
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CPUs
+ Flexible & Cheap
- low performance
GPUs
+ Flexible
- Expensive &
hard to program
Specialized chips/FPGA
+ High Performance
- low flexibility
Flexibility
Performance

7. Available Platforms
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Flexibility
+
Best of 2 worlds
Performance

8. InAccel
helps companies speedup their Spark
applications
by providing ready-to-use
accelerators-as-a-service in the cloud
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9. Acceleration for machine learning
Inaccel offers Accelerators-
as-a-Service for Apache
Spark in the cloud (e.g.
Amazon AWS f1) using
FPGAs
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10. Hardware acceleration
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module filter1 (clock, rst, strm_in, strm_out)
for (i=0; i<NUMUNITS; i=i+1)
always@(posedge clock)
integer i,j; //index for loops
tmp_kernel[j] = k[i*OFFSETX];
FPGA handles compute-
intensive, deeply pipelined,
hardware-accelerated
operations
CPU handles the rest
application
InAccel 800 sec80 sec
200
sec
Source: amazon, Inc.

11. AWS Marketplace
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Amazon EC2 FPGA
Deployment via Marketplace
Amazon
Machine
Image (AMI)
Amazon FPGA Image
(AFI)
AFI is secured, encrypted,
dynamically loaded into the
FPGA - can’t be copied or
downloaded
Customers
AWS Marketplace

12. Accelerators for Spark
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user
c4, m4
s4, …
F1 (FPGA)
Amazon
Marketplace
Download
InAccel Accelerator
from Marketplace
Run your code on CPU
Offload hard
work on F1

13. IP cores available in Amazon AWS
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Logistic Regression K-mean clustering
K-means is one of the
simplest unsupervised
learning algorithms
that solve the well
known clustering
problem.
Gradient Descent IP
block for faster
training of machine
learning algorithms.
Recommendation Engines (ALS
Alternative Least
Square IP core for
the acceleration of
recommendation
engines.
Available in Amazon AWS marketplace for free trial: www.inaccel.com

14. IP Cores
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• Develop hardware
components as IP cores for
widely used applications
• Logistic regression
• Recommendation
• K-means
• Linear regression
• Decision Trees
• NaiveBayes
• …

15. Comparison with AWS c4.large
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• c4 (36 cores)
• m4 (16 cores)
• f1 with our
Accelerator
0
10
20
30
40
50
60
c4 (36) m4 (16) f1 (Accel)
Logistic regression comparison

16. Speedup
• Logistic regression: 2.7x
– 784 features,
– 30 classes
• K-means clustering: 2.8x
– 784 features
– 30 clusters
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17. Acceleration of Logistic Regression
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18. Communication with Host - RDD
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Accelerators for logistic regression/kmeans

19. Available in AWS Marketplace
• IP cores in Amazon AWS marketplace
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20. Zero code changes
• Only replacement of the library is required
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Zero code changes

21. No modification on your code
Only addition of the inaccel library is required
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22. No modification on your code
• Only addition of the inaccel library is required
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23. APIs support:
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24. Demo on Amazon AWS
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Intel 36 cores Xeon on Amazon AWS
c4.8xlarge $1.592/hour
8 cores +
in Amazon AWS FPGA
f1.2xlarge $1.65/hour + inaccel
Note: 4x fast forward for both cases

25. Seamless setup on AWS
1. Executing a single script, will setup Hadoop
and Spark, format the Hadoop Namenode and
upload all the necessary datasets to the hdfs.
2. Start Spark and Hadoop and then your
applications are ready to be accelerated on f1
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26. Documentation
Documentation for all
the APIs
https://inaccel.github.io
• C/C++
• Java
• Python
• Scala
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27. Mllib library
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MLlib contains many algorithms and utilities.
• Classification: logistic regression, naive Bayes,...
• Regression: generalized linear regression, survival
regression,...
• Recommendation: alternating least squares (ALS)
• Clustering: K-means, Gaussian mixtures (GMMs),...
• Decision trees: …

28. Plans for future frameworks
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29. Speedup your application
Contact us if you want to embrace the new
opportunity to speedup your application:
With the same cost
With the same code
info@inaccel.com
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