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1. Foreword
2. Preface
a. Who This Book Is For
b. Who This Book Is Not For
c. What You’ll Learn
d. Python 3
e. Changes from Python 2.7
f. License
g. How to Make an Attribution
h. Errata and Feedback
i. Conventions Used in This Book
j. Using Code Examples
k. O’Reilly Online Learning
l. How to Contact Us
m. Acknowledgments
3. 1. Understanding Performant Python
a. The Fundamental Computer System
i. Computing Units
ii. Memory Units
iii. Communications Layers
b. Putting the Fundamental Elements Together

i. Idealized Computing Versus the Python
Virtual Machine
c. So Why Use Python?
d. How to Be a Highly Performant Programmer
i. Good Working Practices
ii. Some Thoughts on Good Notebook
Practice
iii. Getting the Joy Back into Your Work
4. 2. Profiling to Find Bottlenecks
a. Profiling Efficiently
b. Introducing the Julia Set
c. Calculating the Full Julia Set
d. Simple Approaches to Timing—print and a
Decorator
e. Simple Timing Using the Unix time Command
f. Using the cProfile Module
g. Visualizing cProfile Output with SnakeViz
h. Using line_profiler for Line-by-Line
Measurements
i. Using memory_profiler to Diagnose Memory
Usage
j. Introspecting an Existing Process with PySpy
k. Bytecode: Under the Hood

i. Using the dis Module to Examine
CPython Bytecode
ii. Different Approaches, Different
Complexity
l. Unit Testing During Optimization to Maintain
Correctness
i. No-op @profile Decorator
m. Strategies to Profile Your Code Successfully
n. Wrap-Up
5. 3. Lists and Tuples
a. A More Efficient Search
b. Lists Versus Tuples
i. Lists as Dynamic Arrays
ii. Tuples as Static Arrays
c. Wrap-Up
6. 4. Dictionaries and Sets
a. How Do Dictionaries and Sets Work?
i. Inserting and Retrieving
ii. Deletion
iii. Resizing
iv. Hash Functions and Entropy
b. Dictionaries and Namespaces
c. Wrap-Up

7. 5. Iterators and Generators
a. Iterators for Infinite Series
b. Lazy Generator Evaluation
c. Wrap-Up
8. 6. Matrix and Vector Computation
a. Introduction to the Problem
b. Aren’t Python Lists Good Enough?
i. Problems with Allocating Too Much
c. Memory Fragmentation
i. Understanding perf
ii. Making Decisions with perf’s Output
iii. Enter numpy
d. Applying numpy to the Diffusion Problem
i. Memory Allocations and In-Place
Operations
ii. Selective Optimizations: Finding What
Needs to Be Fixed
e. numexpr: Making In-Place Operations Faster
and Easier
f. A Cautionary Tale: Verify “Optimizations”
(scipy)
g. Lessons from Matrix Optimizations
h. Pandas

i. Pandas’s Internal Model
ii. Applying a Function to Many Rows of
Data
iii. Building DataFrames and Series from
Partial Results Rather than
Concatenating
iv. There’s More Than One (and Possibly a
Faster) Way to Do a Job
v. Advice for Effective Pandas
Development
i. Wrap-Up
9. 7. Compiling to C
a. What Sort of Speed Gains Are Possible?
b. JIT Versus AOT Compilers
c. Why Does Type Information Help the Code
Run Faster?
d. Using a C Compiler
e. Reviewing the Julia Set Example
f. Cython
i. Compiling a Pure Python Version
Using Cython
g. pyximport
i. Cython Annotations to Analyze a Block
of Code
ii. Adding Some Type Annotations

h. Cython and numpy
i. Parallelizing the Solution with OpenMP
on One Machine
i. Numba
i. Numba to Compile NumPy for Pandas
j. PyPy
i. Garbage Collection Differences
ii. Running PyPy and Installing Modules
k. A Summary of Speed Improvements
l. When to Use Each Technology
i. Other Upcoming Projects
m. Graphics Processing Units (GPUs)
i. Dynamic Graphs: PyTorch
ii. Basic GPU Profiling
iii. Performance Considerations of GPUs
iv. When to Use GPUs
n. Foreign Function Interfaces
i. ctypes
ii. cffi
iii. f2py
iv. CPython Module
o. Wrap-Up

10. 8. Asynchronous I/O
a. Introduction to Asynchronous Programming
b. How Does async/await Work?
i. Serial Crawler
ii. Gevent
iii. tornado
iv. aiohttp
c. Shared CPU–I/O Workload
i. Serial
ii. Batched Results
iii. Full Async
d. Wrap-Up
11. 9. The multiprocessing Module
a. An Overview of the multiprocessing Module
b. Estimating Pi Using the Monte Carlo Method
c. Estimating Pi Using Processes and Threads
i. Using Python Objects
ii. Replacing multiprocessing with Joblib
iii. Random Numbers in Parallel Systems
iv. Using numpy
d. Finding Prime Numbers
i. Queues of Work

e. Verifying Primes Using Interprocess
Communication
i. Serial Solution
ii. Naive Pool Solution
iii. A Less Naive Pool Solution
iv. Using Manager.Value as a Flag
v. Using Redis as a Flag
vi. Using RawValue as a Flag
vii. Using mmap as a Flag
viii. Using mmap as a Flag Redux
f. Sharing numpy Data with multiprocessing
g. Synchronizing File and Variable Access
i. File Locking
ii. Locking a Value
h. Wrap-Up
12. 10. Clusters and Job Queues
a. Benefits of Clustering
b. Drawbacks of Clustering
i. $462 Million Wall Street Loss Through
Poor Cluster Upgrade Strategy
ii. Skype’s 24-Hour Global Outage
c. Common Cluster Designs
d. How to Start a Clustered Solution

e. Ways to Avoid Pain When Using Clusters
f. Two Clustering Solutions
i. Using IPython Parallel to Support
Research
ii. Parallel Pandas with Dask
g. NSQ for Robust Production Clustering
i. Queues
ii. Pub/sub
iii. Distributed Prime Calculation
h. Other Clustering Tools to Look At
i. Docker
i. Docker’s Performance
ii. Advantages of Docker
j. Wrap-Up
13. 11. Using Less RAM
a. Objects for Primitives Are Expensive
i. The array Module Stores Many
Primitive Objects Cheaply
ii. Using Less RAM in NumPy with
NumExpr
b. Understanding the RAM Used in a Collection
c. Bytes Versus Unicode
d. Efficiently Storing Lots of Text in RAM

i. Trying These Approaches on 11 Million
Tokens
e. Modeling More Text with Scikit-Learn’s
FeatureHasher
f. Introducing DictVectorizer and FeatureHasher
i. Comparing DictVectorizer and
FeatureHasher on a Real Problem
g. SciPy’s Sparse Matrices
h. Tips for Using Less RAM
i. Probabilistic Data Structures
i. Very Approximate Counting with a 1-
Byte Morris Counter
ii. K-Minimum Values
iii. Bloom Filters
iv. LogLog Counter
v. Real-World Example
14. 12. Lessons from the Field
a. Streamlining Feature Engineering Pipelines
with Feature-engine
i. Feature Engineering for Machine
Learning
ii. The Hard Task of Deploying Feature
Engineering Pipelines
iii. Leveraging the Power of Open Source
Python Libraries

iv. Feature-engine Smooths Building and
Deployment of Feature Engineering
Pipelines
v. Helping with the Adoption of a New
Open Source Package
vi. Developing, Maintaining, and
Encouraging Contribution to Open
Source Libraries
b. Highly Performant Data Science Teams
i. How Long Will It Take?
ii. Discovery and Planning
iii. Managing Expectations and Delivery
c. Numba
i. A Simple Example
ii. Best Practices and Recommendations
iii. Getting Help
d. Optimizing Versus Thinking
e. Adaptive Lab’s Social Media Analytics (2014)
i. Python at Adaptive Lab
ii. SoMA’s Design
iii. Our Development Methodology
iv. Maintaining SoMA
v. Advice for Fellow Engineers

f. Making Deep Learning Fly with
RadimRehurek.com (2014)
i. The Sweet Spot
ii. Lessons in Optimizing
iii. Conclusion
g. Large-Scale Productionized Machine Learning
at Lyst.com (2014)
i. Cluster Design
ii. Code Evolution in a Fast-Moving Start-
Up
iii. Building the Recommendation Engine
iv. Reporting and Monitoring
v. Some Advice
h. Large-Scale Social Media Analysis at Smesh
(2014)
i. Python’s Role at Smesh
ii. The Platform
iii. High Performance Real-Time String
Matching
iv. Reporting, Monitoring, Debugging, and
Deployment
i. PyPy for Successful Web and Data Processing
Systems (2014)
i. Prerequisites
ii. The Database

iii. The Web Application
iv. OCR and Translation
v. Task Distribution and Workers
vi. Conclusion
j. Task Queues at Lanyrd.com (2014)
i. Python’s Role at Lanyrd
ii. Making the Task Queue Performant
iii. Reporting, Monitoring, Debugging, and
Deployment
iv. Advice to a Fellow Developer
15. Index

High Performance Python
SECOND EDITION
Practical Performant Programming for
Humans
Micha Gorelick and Ian Ozsvald

High Performance Python
by Micha Gorelick and Ian Ozsvald
Copyright © 2020 Micha Gorelick and Ian Ozsvald. All rights
reserved.
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Published by O’Reilly Media, Inc., 1005 Gravenstein Highway
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September 2014: First Edition
May 2020: Second Edition
Revision History for the Second Edition

2020-04-30: First release
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High Performance Python is available under the Creative
Commons Attribution-NonCommercial-NoDerivs 3.0
International License.
978-1-492-05502-0
[LSI]

Foreword
When you think about high performance computing, you might
imagine giant clusters of machines modeling complex weather
phenomena or trying to understand signals in data collected
about far-off stars. It’s easy to assume that only people building
specialized systems should worry about the performance
characteristics of their code. By picking up this book, you’ve
taken a step toward learning the theory and practices you’ll
need to write highly performant code. Every programmer can
benefit from understanding how to build performant systems.
There are an obvious set of applications that are just on the
edge of possible, and you won’t be able to approach them
without writing optimally performant code. If that’s your
practice, you’re in the right place. But there is a much broader
set of applications that can benefit from performant code.
We often think that new technical capabilities are what drives
innovation, but I’m equally fond of capabilities that increase the
accessibility of technology by orders of magnitude. When
something becomes ten times cheaper in time or compute
costs, suddenly the set of applications you can address is
wider than you imagined.
The first time this principle manifested in my own work was
over a decade ago, when I was working at a social media
company, and we ran an analysis over multiple terabytes of

data to determine whether people clicked on more photos of
cats or dogs on social media.
It was dogs, of course. Cats just have better branding.
This was an outstandingly frivolous use of compute time and
infrastructure at the time! Gaining the ability to apply
techniques that had previously been restricted to sufficiently
high-value applications, such as fraud detection, to a
seemingly trivial question opened up a new world of now-
accessible possibilities. We were able to take what we learned
from these experiments and build a whole new set of products
in search and content discovery.
For an example that you might encounter today, consider a
machine-learning system that recognizes unexpected animals
or people in security video footage. A sufficiently performant
system could allow you to embed that model into the camera
itself, improving privacy or, even if running in the cloud, using
significantly less compute and power—benefiting the
environment and reducing your operating costs. This can free
up resources for you to look at adjacent problems, potentially
building a more valuable system.
We all desire to create systems that are effective, easy to
understand, and performant. Unfortunately, it often feels like
we have to pick two (or one) out of the three! High
Performance Python is a handbook for people who want to
make things that are capable of all three.

This book stands apart from other texts on the subject in three
ways. First, it’s written for us—humans who write code. You’ll
find all of the context you need to understand why you might
make certain choices. Second, Gorelick and Ozsvald do a
wonderful job of curating and explaining the necessary theory
to support that context. Finally, in this updated edition, you’ll
learn the specific quirks of the most useful libraries for
implementing these approaches today.
This is one of a rare class of programming books that will
change the way you think about the practice of programming.
I’ve given this book to many people who could benefit from the
additional tools it provides. The ideas that you’ll explore in its
pages will make you a better programmer, no matter what
language or environment you choose to work in.
Enjoy the adventure.
Hilary Mason,
Data Scientist in Residence at Accel

Preface
Python is easy to learn. You’re probably here because now that
your code runs correctly, you need it to run faster. You like the
fact that your code is easy to modify and you can iterate with
ideas quickly. The trade-off between easy to develop and runs
as quickly as I need is a well-understood and often-bemoaned
phenomenon. There are solutions.
Some people have serial processes that have to run faster.
Others have problems that could take advantage of multicore
architectures, clusters, or graphics processing units. Some
need scalable systems that can process more or less as
expediency and funds allow, without losing reliability. Others
will realize that their coding techniques, often borrowed from
other languages, perhaps aren’t as natural as examples they
see from others.
In this book we will cover all of these topics, giving practical
guidance for understanding bottlenecks and producing faster
and more scalable solutions. We also include some war stories
from those who went ahead of you, who took the knocks so
you don’t have to.
Python is well suited for rapid development, production
deployments, and scalable systems. The ecosystem is full of

people who are working to make it scale on your behalf,
leaving you more time to focus on the more challenging tasks
around you.

Who This Book Is For
You’ve used Python for long enough to have an idea about why
certain things are slow and to have seen technologies like
Cython, numpy, and PyPy being discussed as possible
solutions. You might also have programmed with other
languages and so know that there’s more than one way to
solve a performance problem.
While this book is primarily aimed at people with CPU-bound
problems, we also look at data transfer and memory-bound
solutions. Typically, these problems are faced by scientists,
engineers, quants, and academics.
We also look at problems that a web developer might face,
including the movement of data and the use of just-in-time (JIT)
compilers like PyPy and asynchronous I/O for easy-win
performance gains.
It might help if you have a background in C (or C++, or maybe
Java), but it isn’t a prerequisite. Python’s most common
interpreter (CPython—the standard you normally get if you type
python at the command line) is written in C, and so the hooks
and libraries all expose the gory inner C machinery. There are
lots of other techniques that we cover that don’t assume any
knowledge of C.
You might also have a lower-level knowledge of the CPU,
memory architecture, and data buses, but again, that’s not

Who This Book Is Not For
This book is meant for intermediate to advanced Python
programmers. Motivated novice Python programmers may be
able to follow along as well, but we recommend having a solid
Python foundation.
We don’t cover storage-system optimization. If you have a SQL
or NoSQL bottleneck, then this book probably won’t help you.
What You’ll Learn
Your authors have been working with large volumes of data, a
requirement for I want the answers faster! and a need for
scalable architectures, for many years in both industry and
academia. We’ll try to impart our hard-won experience to save
you from making the mistakes that we’ve made.
At the start of each chapter, we’ll list questions that the
following text should answer. (If it doesn’t, tell us and we’ll fix it
in the next revision!)
We cover the following topics:
Background on the machinery of a computer so you
know what’s happening behind the scenes
Lists and tuples—the subtle semantic and speed
differences in these fundamental data structures
Dictionaries and sets—memory allocation strategies
and access algorithms in these important data

structures
Iterators—how to write in a more Pythonic way and
open the door to infinite data streams using iteration
Pure Python approaches—how to use Python and its
modules effectively
Matrices with numpy—how to use the beloved numpy
library like a beast
Compilation and just-in-time computing—processing
faster by compiling down to machine code, making
sure you’re guided by the results of profiling
Concurrency—ways to move data efficiently
multiprocessing—various ways to use the built-in
multiprocessing library for parallel computing and
to efficiently share numpy matrices, and some costs
and benefits of interprocess communication (IPC)
Cluster computing—convert your multiprocessing
code to run on a local or remote cluster for both
research and production systems
Using less RAM—approaches to solving large
problems without buying a humungous computer
Lessons from the field—lessons encoded in war stories
from those who took the blows so you don’t have to
Python 3
Python 3 is the standard version of Python as of 2020, with
Python 2.7 deprecated after a 10-year migration process. If
you’re still on Python 2.7, you’re doing it wrong—many libraries

are no longer supported for your line of Python, and support
will become more expensive over time. Please do the
community a favor and migrate to Python 3, and make sure
that all new projects use Python 3.
In this book, we use 64-bit Python. Whilst 32-bit Python is
supported, it is far less common for scientific work. We’d
expect all the libraries to work as usual, but numeric precision,
which depends on the number of bits available for counting, is
likely to change. 64-bit is dominant in this field, along with *nix
environments (often Linux or Mac). 64-bit lets you address
larger amounts of RAM. *nix lets you build applications that can
be deployed and configured in well-understood ways with well-
understood behaviors.
If you’re a Windows user, you’ll have to buckle up. Most of
what we show will work just fine, but some things are OS-
specific, and you’ll have to research a Windows solution. The
biggest difficulty a Windows user might face is the installation
of modules: research in sites like Stack Overflow should give
you the solutions you need. If you’re on Windows, having a
virtual machine (e.g., using VirtualBox) with a running Linux
installation might help you to experiment more freely.
Windows users should definitely look at a packaged solution
like those available through Anaconda, Canopy, Python(x,y), or
Sage. These same distributions will make the lives of Linux
and Mac users far simpler too.

Changes from Python 2.7
If you’ve upgraded from Python 2.7, you might not be aware of
a few relevant changes:
/ meant integer division in Python 2.7, whereas it
performs float division in Python 3.
str and unicode were used to represent text data in
Python 2.7; in Python 3, everything is a str, and these
are always Unicode. For clarity, a bytes type is used if
we’re using unencoded byte sequences.
If you’re in the process of upgrading your code, two good
guides are “Porting Python 2 Code to Python 3” and
“Supporting Python 3: An in-depth guide”. With a distribution
like Anaconda or Canopy, you can run both Python 2 and
Python 3 simultaneously—this will simplify your porting.
License
This book is licensed under Creative Commons Attribution-
NonCommercial-NoDerivs 3.0.
You’re welcome to use this book for noncommercial purposes,
including for noncommercial teaching. The license allows only
for complete reproductions; for partial reproductions, please
contact O’Reilly (see “How to Contact Us”). Please attribute the
book as noted in the following section.

We negotiated that the book should have a Creative Commons
license so the contents could spread further around the world.
We’d be quite happy to receive a beer if this decision has
helped you. We suspect that the O’Reilly staff would feel
similarly about the beer.
How to Make an Attribution
The Creative Commons license requires that you attribute your
use of a part of this book. Attribution just means that you
should write something that someone else can follow to find
this book. The following would be sensible: “High Performance
Python, 2nd ed., by Micha Gorelick and Ian Ozsvald (O’Reilly).
Copyright 2020 Micha Gorelick and Ian Ozsvald, 978-1-492-
05502-0.”
Errata and Feedback
We encourage you to review this book on public sites like
Amazon—please help others understand if they would benefit
from this book! You can also email us at
feedback@highperformancepython.com.
We’re particularly keen to hear about errors in the book,
successful use cases where the book has helped you, and high
performance techniques that we should cover in the next
edition. You can access the web page for this book at
https://oreil.ly/high-performance-python-2e.

Complaints are welcomed through the instant-complaint-
transmission-service > /dev/null.
Conventions Used in This Book
The following typographical conventions are used in this book:
Italic
Indicates new terms, URLs, email addresses, filenames,
and file extensions.
Constant width
Used for program listings, as well as within paragraphs to
refer to program elements such as variable or function
names, databases, datatypes, environment variables,
statements, and keywords.
Constant width bold
Shows commands or other text that should be typed literally
by the user.
Constant width italic
Shows text that should be replaced with user-supplied
values or by values determined by context.
TIP
This element signifies a tip, suggestion, or critical thinking question.

NOTE
This element signifies a general note.
WARNING
This element indicates a warning or caution.
Using Code Examples
Supplemental material (code examples, exercises, etc.) is
available for download at
https://github.com/mynameisfiber/high_performance_python_2
e.
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Acknowledgments
Hilary Mason wrote our foreword—thanks for composing such
a wonderful opening narrative for our book. Giles Weaver and
Dimitri Denisjonok provided invaluable technical feedback on
this edition; great work, chaps.
Thanks to Patrick Cooper, Kyran Dale, Dan Foreman-Mackey,
Calvin Giles, Brian Granger, Jamie Matthews, John

Montgomery, Christian Schou Oxvig, Matt “snakes” Reiferson,
Balthazar Rouberol, Michael Skirpan, Luke Underwood, Jake
Vanderplas, and William Winter for invaluable feedback and
contributions.
Ian thanks his wife, Emily, for letting him disappear for another
eight months to write this second edition (thankfully, she’s
terribly understanding). Ian apologizes to his dog for sitting and
writing rather than walking in the woods quite as much as she’d
have liked.
Micha thanks Marion and the rest of his friends and family for
being so patient while he learned to write.
O’Reilly editors are rather lovely to work with; do strongly
consider talking to them if you want to write your own book.
Our contributors to the “Lessons from the Field” chapter very
kindly shared their time and hard-won lessons. We give thanks
to Soledad Galli, Linda Uruchurtu, Vanentin Haenel, and
Vincent D. Warmerdam for this edition and to Ben Jackson,
Radim Řehůřek, Sebastjan Trepca, Alex Kelly, Marko Tasic,
and Andrew Godwin for their time and effort during the
previous edition.

Chapter 1. Understanding
Performant Python
QUESTIONS YOU’LL BE ABLE TO ANSWER AFTER THIS
CHAPTER
What are the elements of a computer’s architecture?
What are some common alternate computer architectures?
How does Python abstract the underlying computer architecture?
What are some of the hurdles to making performant Python code?
What strategies can help you become a highly performant programmer?
Programming computers can be thought of as moving bits of
data and transforming them in special ways to achieve a
particular result. However, these actions have a time cost.
Consequently, high performance programming can be thought
of as the act of minimizing these operations either by reducing
the overhead (i.e., writing more efficient code) or by changing
the way that we do these operations to make each one more
meaningful (i.e., finding a more suitable algorithm).
Let’s focus on reducing the overhead in code in order to gain
more insight into the actual hardware on which we are moving
these bits. This may seem like a futile exercise, since Python
works quite hard to abstract away direct interactions with the
hardware. However, by understanding both the best way that

bits can be moved in the real hardware and the ways that
Python’s abstractions force your bits to move, you can make
progress toward writing high performance programs in Python.
The Fundamental Computer System
The underlying components that make up a computer can be
simplified into three basic parts: the computing units, the
memory units, and the connections between them. In addition,
each of these units has different properties that we can use to
understand them. The computational unit has the property of
how many computations it can do per second, the memory unit
has the properties of how much data it can hold and how fast
we can read from and write to it, and finally, the connections
have the property of how fast they can move data from one
place to another.
Using these building blocks, we can talk about a standard
workstation at multiple levels of sophistication. For example,
the standard workstation can be thought of as having a central
processing unit (CPU) as the computational unit, connected to
both the random access memory (RAM) and the hard drive as
two separate memory units (each having different capacities
and read/write speeds), and finally a bus that provides the
connections between all of these parts. However, we can also
go into more detail and see that the CPU itself has several
memory units in it: the L1, L2, and sometimes even the L3 and
L4 cache, which have small capacities but very fast speeds
(from several kilobytes to a dozen megabytes). Furthermore,

new computer architectures generally come with new
configurations (for example, Intel’s SkyLake CPUs replaced the
frontside bus with the Intel Ultra Path Interconnect and
restructured many connections). Finally, in both of these
approximations of a workstation we have neglected the
network connection, which is effectively a very slow connection
to potentially many other computing and memory units!
To help untangle these various intricacies, let’s go over a brief
description of these fundamental blocks.
Computing Units
The computing unit of a computer is the centerpiece of its
usefulness—it provides the ability to transform any bits it
receives into other bits or to change the state of the current
process. CPUs are the most commonly used computing unit;
however, graphics processing units (GPUs) are gaining
popularity as auxiliary computing units. They were originally
used to speed up computer graphics but are becoming more
applicable for numerical applications and are useful thanks to
their intrinsically parallel nature, which allows many
calculations to happen simultaneously. Regardless of its type,
a computing unit takes in a series of bits (for example, bits
representing numbers) and outputs another set of bits (for
example, bits representing the sum of those numbers). In
addition to the basic arithmetic operations on integers and real
numbers and bitwise operations on binary numbers, some
computing units also provide very specialized operations, such

as the “fused multiply add” operation, which takes in three
numbers, A, B, and C, and returns the value A * B + C.
The main properties of interest in a computing unit are the
number of operations it can do in one cycle and the number of
cycles it can do in one second. The first value is measured by
its instructions per cycle (IPC), while the latter value is
measured by its clock speed. These two measures are always
competing with each other when new computing units are
being made. For example, the Intel Core series has a very high
IPC but a lower clock speed, while the Pentium 4 chip has the
reverse. GPUs, on the other hand, have a very high IPC and
clock speed, but they suffer from other problems like the slow
communications that we discuss in “Communications Layers”.
Furthermore, although increasing clock speed almost
immediately speeds up all programs running on that
computational unit (because they are able to do more
calculations per second), having a higher IPC can also
drastically affect computing by changing the level of
vectorization that is possible. Vectorization occurs when a CPU
is provided with multiple pieces of data at a time and is able to
operate on all of them at once. This sort of CPU instruction is
known as single instruction, multiple data (SIMD).
In general, computing units have advanced quite slowly over
the past decade (see Figure 1-1). Clock speeds and IPC have
both been stagnant because of the physical limitations of
making transistors smaller and smaller. As a result, chip
1

manufacturers have been relying on other methods to gain
more speed, including simultaneous multithreading (where
multiple threads can run at once), more clever out-of-order
execution, and multicore architectures.
Hyperthreading presents a virtual second CPU to the host
operating system (OS), and clever hardware logic tries to
interleave two threads of instructions into the execution units
on a single CPU. When successful, gains of up to 30% over a
single thread can be achieved. Typically, this works well when
the units of work across both threads use different types of
execution units—for example, one performs floating-point
operations and the other performs integer operations.
Out-of-order execution enables a compiler to spot that some
parts of a linear program sequence do not depend on the
results of a previous piece of work, and therefore that both
pieces of work could occur in any order or at the same time. As
long as sequential results are presented at the right time, the
program continues to execute correctly, even though pieces of
work are computed out of their programmed order. This
enables some instructions to execute when others might be
blocked (e.g., waiting for a memory access), allowing greater
overall utilization of the available resources.
Finally, and most important for the higher-level programmer,
there is the prevalence of multicore architectures. These
architectures include multiple CPUs within the same unit, which
increases the total capability without running into barriers to

making each individual unit faster. This is why it is currently
hard to find any machine with fewer than two cores—in this
case, the computer has two physical computing units that are
connected to each other. While this increases the total number
of operations that can be done per second, it can make writing
code more difficult!
Figure 1-1. Clock speed of CPUs over time (from CPU DB)
Simply adding more cores to a CPU does not always speed up
a program’s execution time. This is because of something
known as Amdahl’s law. Simply stated, Amdahl’s law is this: if
a program designed to run on multiple cores has some
subroutines that must run on one core, this will be the limitation
for the maximum speedup that can be achieved by allocating
more cores.
For example, if we had a survey we wanted one hundred
people to fill out, and that survey took 1 minute to complete, we
could complete this task in 100 minutes if we had one person
asking the questions (i.e., this person goes to participant 1,
asks the questions, waits for the responses, and then moves to
participant 2). This method of having one person asking the
questions and waiting for responses is similar to a serial
process. In serial processes, we have operations being
satisfied one at a time, each one waiting for the previous
operation to complete.
However, we could perform the survey in parallel if we had two
people asking the questions, which would let us finish the

process in only 50 minutes. This can be done because each
individual person asking the questions does not need to know
anything about the other person asking questions. As a result,
the task can easily be split up without having any dependency
between the question askers.
Adding more people asking the questions will give us more
speedups, until we have one hundred people asking questions.
At this point, the process would take 1 minute and would be
limited simply by the time it takes a participant to answer
questions. Adding more people asking questions will not result
in any further speedups, because these extra people will have
no tasks to perform—all the participants are already being
asked questions! At this point, the only way to reduce the
overall time to run the survey is to reduce the amount of time it
takes for an individual survey, the serial portion of the problem,
to complete. Similarly, with CPUs, we can add more cores that
can perform various chunks of the computation as necessary
until we reach a point where the bottleneck is the time it takes
for a specific core to finish its task. In other words, the
bottleneck in any parallel calculation is always the smaller
serial tasks that are being spread out.
Furthermore, a major hurdle with utilizing multiple cores in
Python is Python’s use of a global interpreter lock (GIL). The
GIL makes sure that a Python process can run only one
instruction at a time, regardless of the number of cores it is
currently using. This means that even though some Python
code has access to multiple cores at a time, only one core is

running a Python instruction at any given time. Using the
previous example of a survey, this would mean that even if we
had 100 question askers, only one person could ask a question
and listen to a response at a time. This effectively removes any
sort of benefit from having multiple question askers! While this
may seem like quite a hurdle, especially if the current trend in
computing is to have multiple computing units rather than
having faster ones, this problem can be avoided by using other
standard library tools, like multiprocessing (Chapter 9),
technologies like numpy or numexpr (Chapter 6), Cython
(Chapter 7), or distributed models of computing (Chapter 10).
NOTE
Python 3.2 also saw a major rewrite of the GIL, which made the system
much more nimble, alleviating many of the concerns around the system
for single-thread performance. Although it still locks Python into running
only one instruction at a time, the GIL now does better at switching
between those instructions and doing so with less overhead.
Memory Units
Memory units in computers are used to store bits. These could
be bits representing variables in your program or bits
representing the pixels of an image. Thus, the abstraction of a
memory unit applies to the registers in your motherboard as
well as your RAM and hard drive. The one major difference
between all of these types of memory units is the speed at
which they can read/write data. To make things more

complicated, the read/write speed is heavily dependent on the
way that data is being read.
For example, most memory units perform much better when
they read one large chunk of data as opposed to many small
chunks (this is referred to as sequential read versus random
data). If the data in these memory units is thought of as pages
in a large book, this means that most memory units have better
read/write speeds when going through the book page by page
rather than constantly flipping from one random page to
another. While this fact is generally true across all memory
units, the amount that this affects each type is drastically
different.
In addition to the read/write speeds, memory units also have
latency, which can be characterized as the time it takes the
device to find the data that is being used. For a spinning hard
drive, this latency can be high because the disk needs to
physically spin up to speed and the read head must move to
the right position. On the other hand, for RAM, this latency can
be quite small because everything is solid state. Here is a short
description of the various memory units that are commonly
found inside a standard workstation, in order of read/write
speeds:
Spinning hard drive
Long-term storage that persists even when the computer is
shut down. Generally has slow read/write speeds because
the disk must be physically spun and moved. Degraded
2

performance with random access patterns but very large
capacity (10 terabyte range).
Solid-state hard drive
Similar to a spinning hard drive, with faster read/write
speeds but smaller capacity (1 terabyte range).
RAM
Used to store application code and data (such as any
variables being used). Has fast read/write characteristics
and performs well with random access patterns, but is
generally limited in capacity (64 gigabyte range).
L1/L2 cache
Extremely fast read/write speeds. Data going to the CPU
must go through here. Very small capacity (megabytes
range).
Figure 1-2 gives a graphic representation of the differences
between these types of memory units by looking at the
characteristics of currently available consumer hardware.
A clearly visible trend is that read/write speeds and capacity
are inversely proportional—as we try to increase speed,
capacity gets reduced. Because of this, many systems
implement a tiered approach to memory: data starts in its full
state in the hard drive, part of it moves to RAM, and then a
much smaller subset moves to the L1/L2 cache. This method
of tiering enables programs to keep memory in different places
depending on access speed requirements. When trying to
optimize the memory patterns of a program, we are simply

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(Ebook) High Performance Python by Micha Gorelick, Ian Ozsvald

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