Back to Basics Lambda Expressions Barbara Geller & Ansel Sermersheim CppCon September 2020Introduction ● Prologue ● History ● Function Pointer ● Function Object ● Definition of a Lambda Expression slot library ○ CsLibGuarded ■ library for managing access to data shared between threadsLambda Expressions ● History ○ lambda calculus is a branch of mathematics ■ introduced in the 1930’s to prove memory ■ languages which were influenced by lambda calculus ● Haskell, LISP, and ML 5Lambda Expressions ● History ○ why do we use the terminology lambda expression ■ greek letter λ refers to an anonymous

About me 2 6 ways for implementing math expressions calculator, Amir Kirsh Lecturer Academic College of Tel-Aviv-Yaffo and Tel-Aviv University Developer Advocate at Member of the Israeli ISO C++ NB NB Co-Organizer of the CoreCpp conference and meetup group3 6 ways for implementing math expressions calculator, Amir Kirsh Suffering from slow builds? It’s not just waste of time It affects your dev implementing math expressions calculator, Amir KirshSuccess Stories 5 6 ways for implementing math expressions calculator, Amir KirshSuccess Stories 6 6 ways for implementing math expressions calculator, Amir

Comparison Binding Constraints Architecture Substitution Construction Conclusion Mathematical expressions Normal distribution PDF f = 1 √ 2πσ2 e− (x−µ)2 2σ2 2 + + 2 Normal distribution Legend Function requalify_as_const : std:: conditional < 3 std :: is_lvalue_reference_v , 4 std :: add_lvalue_reference_t >>, 5 std :: conditional_t < 6 std :: is_rvalue_reference_v Architecture Substitution Construction Conclusion Concepts: the constructions (expressions) y(t) = a × sin (ω × t + ϕ) Symbolic expressions term: “a mathematical object”: ϕ, ω, ω × t + ϕ, . . . formula: “a mathematical

for consumers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 9.2 Conditional settings, options and requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91 2@user/testing", "ToolB/0.2@user/testing" def build_requirements(self): # useful for example for conditional build_requires # This means, if we are running on a Windows machine, require ToolWin if platform be checked for existence (whether they’ve been applied) in the recipes, which can be useful for conditional logic in the recipes. In this example, we could have one recipe with the following build() method:

for consumers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94 9.3 Conditional settings, options and requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96 2@user/testing", "ToolB/0.2@user/testing" def build_requirements(self): # useful for example for conditional build_requires # This means, if we are running on a Windows machine, require ToolWin if platform be checked for existence (whether they’ve been applied) in the recipes, which can be useful for conditional logic in the recipes. In this example, we could have one recipe with the following build() method:

CPU – Pipelining – Branch prediction and hardware loop unrolling ● Conditional code vs efficiency ● Optimizing conditional code ● Branchless programmingWHAT CAN BRANCHLESS OPTIMIZATIONS DO? f(bool a continuous stream of instructions ● Instructions are fetched, decoded, and executed ● Conditional jumps (branches) disrupt that order ● CPU must wait until it knows which instruction to fetch true ... load:v2[i+1] load:v1[i+1] ... a[i]:a+=v2[i] ... ... ... ... ... conditional move x86 cmove conditional move x86 cmoveBranchless Computing 23 BRANCHES: BANE OF THE PIPELINES ● Well-pipelined

for consumers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95 10.2 Conditional settings, options and requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 2@user/testing", "ToolB/0.2@user/testing" def build_requirements(self): # useful for example for conditional build_requires # This means, if we are running on a Windows machine, require ToolWin if platform be checked for existence (whether they’ve been applied) in the recipes, which can be useful for conditional logic in the recipes. In this example, we could have one recipe with the following build() method:

for consumers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 10.2 Conditional settings, options and requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 2@user/testing", "ToolB/0.2@user/testing" def build_requirements(self): # useful for example for conditional build_requires # This means, if we are running on a Windows machine, require ToolWin if platform be checked for existence (whether they’ve been applied) in the recipes, which can be useful for conditional logic in the recipes. In this example, we could have one recipe with the following build() method:

for consumers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 9.2 Conditional settings, options and requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95 2@user/testing", "ToolB/0.2@user/testing" def build_requirements(self): # useful for example for conditional build_requires # This means, if we are running on a Windows machine, require ToolWin if platform be checked for existence (whether they’ve been applied) in the recipes, which can be useful for conditional logic in the recipes. In this example, we could have one recipe with the following build() method:

for consumers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 9.2 Conditional settings, options and requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95 2@user/testing", "ToolB/0.2@user/testing" def build_requirements(self): # useful for example for conditional build_requires # This means, if we are running on a Windows machine, require ToolWin if platform be checked for existence (whether they’ve been applied) in the recipes, which can be useful for conditional logic in the recipes. In this example, we could have one recipe with the following build() method: