Speaker 1: So the processor in your smartphone is incredibly fast, like
Speaker 1: unbelievably fast. Oh yeah, absolutely, But if you look at
Speaker 1: single core clock speeds, that single processor hasn't actually gotten
Speaker 1: any faster in like almost twenty years.
Speaker 2: Right, which is wild to think.
Speaker 1: About, it is, and the reason why it's well, it's
Speaker 1: physically impossible to beat the speed of light.
Speaker 2: Yeah, that limitation forced a massive pivot in hardware engineering
Speaker 2: that honestly, most people never even noticed. Single cores just
Speaker 2: stopped accelerating and instead they started multiplying, like we went
Speaker 2: from one core to four to sixteen, and suddenly developers
Speaker 2: were handed this completely different architectural universe to build on.
Speaker 2: It's a shift just as significant as the Industrial Revolution, honestly,
Speaker 2: but it's happening silently right inside our devices.
Speaker 1: Welcome to this deep dive today. We are unpacking excerpts
Speaker 1: from the book Concurrency in dot Net Modern Patterns by
Speaker 1: Ricardo Terrell. It's a great book, yeah, and our mission
Speaker 1: today is to understand exactly why our hardware hit this
Speaker 1: literal physical wall and how the entire way we write software,
Speaker 1: like the invisible architecture holding our digital lives together, has
Speaker 1: to radically shift toward concurrency and functional programming to overcome it.
Speaker 2: Exactly, because if we don't change how we code, all
Speaker 2: that multi core computing potential goes entirely to waste.
Speaker 1: It really does.
Speaker 2: To really wrap our heads around the software shift, we
Speaker 2: have to look at the physics that broke Moore's law.
Speaker 2: You know, for decades, transistor density doubled and CPU speeds
Speaker 2: just kept climbing and climbing.
Speaker 1: Right the golden age of computing.
Speaker 2: Yeah, but right around two thousand and five, single core
Speaker 2: clock speeds just slammed into a plateau at around three
Speaker 2: point five gigahers.
Speaker 1: Okay, let's unpack this because the scale here is just
Speaker 1: mind blowing. At a three point five gigahertz clock speed,
Speaker 1: a processor is completing one cycle every two point eight
Speaker 1: five nanoseconds. And we know that nothing in the universe,
Speaker 1: including the electrical data signals moving across the silk and chip,
Speaker 1: can travel faster than light.
Speaker 2: Wait, so you're telling me the literal physical laws of
Speaker 2: the universe, like the speed of light is the only
Speaker 2: reason my smartphone's single processor isn't getting faster.
Speaker 1: If we connect this to the bigger picture, yes, exactly.
Speaker 1: See in a vacuum, light travels roughly one foot per nanosecond. Okay,
Speaker 1: but inside a silicon medium, it's actually a bit slower.
Speaker 1: So in that tiny two point eighty five nanosecond window,
Speaker 1: a signal can only physically propagate about thirty centimeters.
Speaker 2: Wow.
Speaker 1: Now, if hardware engineers wanted to push a CPU to say,
Speaker 1: one hundred gigahertz, the cycle time drops to point zero
Speaker 1: one nanoseconds.
Speaker 2: Which means the signal only has time to travel about
Speaker 2: three milimeters exactly. So the entire CPU core would have
Speaker 2: to be manufactured at point three millimeters in size just
Speaker 2: for the signals to reach the other side of the
Speaker 2: chip in time for the next clock cycle.
Speaker 1: Right, and cramming that much switching frequency into a space
Speaker 1: literally a fraction of a millimeter wide creates well catastrophic
Speaker 1: energy dissipation because of the sheet.
Speaker 2: Yeah, the heat generated would literally melt the crystal structure
Speaker 2: of the chip. So hardware engineers realized they couldn't rewrite
Speaker 2: the laws of physics, and they couldn't magically stop thermal
Speaker 2: dynamics from existing.
Speaker 1: Right, So their solution was to stop trying to make
Speaker 1: one single core infinitely faster and just start stamping out
Speaker 1: more of them on the same die.
Speaker 2: Exactly, They just give us more cores. But this dumps
Speaker 2: a massive problem directly into the lapse of software developers.
Speaker 1: Which brings up this really great coffee shop analogy from
Speaker 1: the source material to explain how software historically handled tasks
Speaker 1: versus how it has to handle them.
Speaker 2: Now, oh, I love this analogy.
Speaker 1: Yeah. So, if you have one barista and they work sequentially,
Speaker 1: they grind the coffee, brew it, steam the milk, and
Speaker 1: combine it one instruction at a time. It's safe, but it's.
Speaker 2: Really slow, right, So if that barista decides to multitask,
Speaker 2: they start the espresso dripping, spin around, and immediately start
Speaker 2: steaming the milk.
Speaker 1: Like time slicing exactly.
Speaker 2: Yeah, they aren't doing two things simultaneously, but the operating
Speaker 2: system uses time slicing to which their context between tasks
Speaker 2: so fast it utilizes the downtime.
Speaker 1: But the real leap is true parallelism. The manager just
Speaker 1: hires a second barista and builds a completely second espresso station.
Speaker 1: Now you have two independent actors making two completely different
Speaker 1: drinks at the exact same millisecond. And to show the
Speaker 1: scale of this parallel explosion, the source points to Pixar.
Speaker 2: This is a crazy stat it is.
Speaker 1: Rendering the original Toy Story movie in nineteen ninety five
Speaker 1: required a server farm of one hundred processors. Processing a
Speaker 1: modern animated movie today it takes twenty four thousand cores.
Speaker 2: Yeah, and that explosion in parallel processing power is exactly
Speaker 2: why software has to change. If you take a legacy
Speaker 2: sequential application like code designed for one barista and run
Speaker 2: it on a modern machine with eight cores, you have
Speaker 2: one thread working frantically while seven other cores just sit there.
Speaker 1: Idle, totally wasted. Exactly, So the obvious answer seems to be,
Speaker 1: you know, just divide the workup, run the software in parallel.
Speaker 1: But let's talk about what happens when you take standard
Speaker 1: object or code and toss it into a multi core environment.
Speaker 1: You have independent threads trying to modify a shared state,
Speaker 1: and it turns into an absolute disaster.
Speaker 2: Oh, a total nightmare, because mutable state is the core
Speaker 2: philosophy of traditional imperative programming. Variables are designed to be
Speaker 2: changed over time. But when two threads hit the exact
Speaker 2: same memory address at the exact same microsecond, you get
Speaker 2: race conditions.
Speaker 1: The book illustrates this beautifully imagine a variable X sitting
Speaker 1: in memory with a value of forty two. Thread one
Speaker 1: and thread two both need to add one to X.
Speaker 2: Okay, in a.
Speaker 1: Perfect sequence, thread one adds one to make it forty three,
Speaker 1: and then thread two comes in adds one and makes
Speaker 1: it forty four. But in a parallel world they overlap.
Speaker 2: Yeah, so thread one reads the value forty two from memory,
Speaker 2: but before thread one can finish its calculation and save
Speaker 2: the new value, thread two swoops in and reads the
Speaker 2: exact same memory address.
Speaker 1: It also sees forty two exactly, so thread one writes
Speaker 1: forty three back to memory, but thread two is completely
Speaker 1: oblivious to that. It adds one to its own original
Speaker 1: read to forty two and just overwrites the memory address
Speaker 1: with forty three again.
Speaker 2: Right, the data is completely corrupted and entire calculation was
Speaker 2: just swallowed hole by the system.
Speaker 1: So it's like two people trying to edit the exact
Speaker 1: same sentence in a shared document at the exact same millisecond.
Speaker 2: Yes, that's exactly what it's like. And to prevent that
Speaker 2: document overright, developers historically introduced locks known as mutual exclusion. Okay,
Speaker 2: the padlocks, Right, A thread places a lock on the variable,
Speaker 2: barring any other thread from touching it until the operation completes.
Speaker 2: But locking a resource creates an entirely new hazard, which
Speaker 2: is deadlocks.
Speaker 1: Oh man, so we give them padlocks, but then they
Speaker 1: just accidentally lock each other out of the building entirely.
Speaker 2: Pretty much, it's the ultimate software traffic jam.
Speaker 1: How does that happen?
Speaker 2: Well, Thread one locks resource A, but realizes it needs
Speaker 2: resource B to finish its execution. Meanwhile, thread two has
Speaker 2: already locked resource B, but it needs resource A to
Speaker 2: finish its job. Oh no, Thread one sits idle waiting
Speaker 2: on thread too. Thread two sits idle waiting on thread one.
Speaker 2: They are locked in a circular weight for eternity, and
Speaker 2: the entire application just freezes.
Speaker 1: Okay, So if you're listening to this and thinking, well,
Speaker 1: I just won't share mutable data between my threads, let's
Speaker 1: put that to the test. Take a task that divides
Speaker 1: data up perfectly, like sorting a massive list. Quick sort
Speaker 1: is a classic algorithm that picks a pivot point and
Speaker 1: splits a lift into two independent halves. There is no
Speaker 1: shared state to lock, so we should be able to
Speaker 1: just split that list up throw one hundred threads at it.
Speaker 1: And watch it fly.
Speaker 2: And that is the exact logical assumption developers make when
Speaker 2: transitioning to multi coore. But Ricardo Terrell tested this assumption
Speaker 2: with a pretty brutal benchmark. Oh he took a list
Speaker 2: of one million random items and sorted it sequentially on
Speaker 2: a single thread. It took six point five seconds.
Speaker 1: Okay. Then he parallelized it using Microsoft's task parallel library. Yep,
Speaker 1: every single time the algorithm split the list into two
Speaker 1: smaller halves, it'spond a brand new thread to handle the
Speaker 1: new split. So we're taking a six point five second
Speaker 1: sequential task and spreading it across all the CPU core simultaneously.
Speaker 1: But I'm looking at this kicksort benchmark from the source
Speaker 1: and this makes zero sense. The processing time almost double
Speaker 1: to over twelve seconds. Why is the hardware actively punishing
Speaker 1: the code?
Speaker 2: Well, the hardware isn't punishing the code. The operating system
Speaker 2: is drowning and administrative paperwork. What the developer failed to
Speaker 2: account for is thread overhead. Creating a thread, allocating a
Speaker 2: memory block for its call stack, and scheduling it on
Speaker 2: the CPU. All of that takes computational effort.
Speaker 1: Here's where it gets really interesting. This is exactly like
Speaker 1: hiring one thousand chefs to make a single Omelet the
Speaker 1: administrative overhead of them bumping into each other, communicating and
Speaker 1: figuring out who gets to use this batchela takes far
Speaker 1: longer than just letting one chef cook. You have to
Speaker 1: balance the workforce.
Speaker 2: That is a perfect analogy. If the actual sorting work
Speaker 2: assigned to the thread takes less time than the administrative
Speaker 2: effort of creating the thread itself, your performance absolutely plummets. Wow,
Speaker 2: parallelism isn't magic, you know. It is a resource that
Speaker 2: requires strict budgeting. There is an inherent limitation to how
Speaker 2: much extra threading actually helps.
Speaker 1: So how does the book solve the quick.
Speaker 2: Sort disaster by limiting the depth of the recursion. Terrell
Speaker 2: introduces a specific mathematical formula to cap the thread creation.
Speaker 2: It's the base two logarithm of the processor count plus four.
Speaker 1: Okay, bath time.
Speaker 2: Yeah, So, if you are running a four corps machine,
Speaker 2: this formula tells the program to completely stop spawning new
Speaker 2: threads once it reaches roughly sixteen times the processor.
Speaker 1: Count, meaning you hit a hard ceiling about thirty six
Speaker 1: to sixty four concurrent tasks exactly essentially cap the number
Speaker 1: of chefs in the kitchen. Right.
Speaker 2: Once the algorithm hits that cap, the remaining small chunks
Speaker 2: of the list are simply sorted sequentially by the existing threads.
Speaker 1: Oh that smart.
Speaker 2: Yeah, the administrative overhead vanishes, the CPU cores remain fully
Speaker 2: saturated with actual work, and the sorting time drops dramatically
Speaker 2: from the original six point five seconds down to three
Speaker 2: point five seconds.
Speaker 1: That is incredible. So traditional locks freeze the system with
Speaker 1: dead locks, and unbounded threading chokes the CPU with overhead. Ye,
Speaker 1: how do we actually write concurrent code that scales safely
Speaker 1: and efficiently. This is where the deep dive transitions to
Speaker 1: the overarching solution provided by the source material, which is
Speaker 1: functional programming.
Speaker 2: Yes, functional programming abandons mutable state entirely. Instead of trying
Speaker 2: to build complex, fragile guardrails around our variables to keep
Speaker 2: them safe from multiple threads, FP asks a much more
Speaker 2: radical question, what if variables are never allowed to change?
Speaker 1: Wait, a world without variables. That sounds like trying to
Speaker 1: do math without an eraser, Right, how do you actually
Speaker 1: get anything done if you can't change the state of anything.
Speaker 1: If I have a user profile and the user has
Speaker 1: a birthday, their age has to update from twenty nine
Speaker 1: to thirty. How do you accomplish that without altering the
Speaker 1: original variable.
Speaker 2: What's fascinating here is that in functional programming you rely
Speaker 2: on transformations rather than mutations.
Speaker 1: Okay, transformations.
Speaker 2: Yeah. You pass the original user profile into a pure function.
Speaker 2: That function takes the data, applies the update, and returns
Speaker 2: a brand new, complete copy of the profile with the
Speaker 2: age set to thirty.
Speaker 1: Oh. I see, the.
Speaker 2: Original profile remains permanently locked in memory exactly as it was.
Speaker 2: Threads can't break what they can't.
Speaker 1: Change, and because that original data never changes, it doesn't
Speaker 1: matter if ten thousand other threads are reading it at
Speaker 1: the exact same time. No thread can ever accidentally overwrite
Speaker 1: another thread's work exactly.
Speaker 2: If there is no mutable state, race conditions simply cease
Speaker 2: to exist. Wow, and without race conditions, we have no
Speaker 2: need for mutual exclusion locks, and without locks, deadlocks are
Speaker 2: mathematically impossible.
Speaker 1: It is huge.
Speaker 2: It is the entire category of concurrency hazards that plague
Speaker 2: object oriented developers. Just vanishes.
Speaker 1: The source material tightly couples this immutability to the concept
Speaker 1: of pure functions and referential transparency. Right.
Speaker 2: Yeah, A pure function operates in total isolation. It has
Speaker 2: absolutely zero side effects the none. It doesn't query a database,
Speaker 2: it doesn't mutate a global variable. It doesn't even print
Speaker 2: a log to the screen. Its only purpose is to
Speaker 2: map an input to an output.
Speaker 1: Which leads to referential transparency. If you pass a pure
Speaker 1: function the exact same input, it is guaranteed to return
Speaker 1: the exact same output every single time, regardless of the
Speaker 1: system's global state exactly. That predictability is what makes it
Speaker 1: so robust for multiple cores to execute simultaneously.
Speaker 2: And this rigid predictability also eliminates entire classes of runtime errors.
Speaker 2: The book brings up a notorious piece of computer science
Speaker 2: history to highlight this, Tonyhorre's nineteen sixty five invention of
Speaker 2: the null reference.
Speaker 1: The billion dollar mistake. In traditional imperative languages, a variable
Speaker 1: can point to a valid piece of data, or it
Speaker 1: can point to null, which is literally nothing, right, and
Speaker 1: if your application attempts to access a very expecting an
Speaker 1: integer or an object, but encounter as a null, the
Speaker 1: whole system throws an exception and crashes.
Speaker 2: But functional language is like f sharp strongly discouraged nulls.
Speaker 2: They actually force developers to handle the potential absence of
Speaker 2: data explicitly using specialized immutable structures. Oh wow, Yeah, you
Speaker 2: cannot even compile the program until you have proven to
Speaker 2: the compiler that you have handled the nothing case. It
Speaker 2: wipes out countless bugs before the code is even executed.
Speaker 1: Now, if you're listening to this and thinking, okay, immutability
Speaker 1: is great for safety, But how do I actually build
Speaker 1: a massive complex application if every function is just an
Speaker 1: isolated island returning copies of data.
Speaker 2: It's a fair question.
Speaker 1: We need to talk about the two main tools functional
Speaker 1: programming uses to bridge that gap, composition enclosures. Let's start
Speaker 1: with composition.
Speaker 2: So, because pure functions strictly take inputs and return outputs,
Speaker 2: they act as perfectly standardized building blocks. Function composition is
Speaker 2: the mechanism of gluing these small, isolated functions together to
Speaker 2: construct complex data pipelines.
Speaker 1: The source uses a coffee brewing example for this, which
Speaker 1: I love. Say you have a function called grind coffee
Speaker 1: that takes raw beans and outputs grounds, okay, and a
Speaker 1: separate function called brew coffee that takes grounds and outputs espresso.
Speaker 1: In traditional listic code, you have to write it inside
Speaker 1: out something like brew coffee parenthesis, grind coffee parenthesis beans.
Speaker 1: You practically have to read the syntax backwards to trace
Speaker 1: the logic.
Speaker 2: Right, it gets messy, but functional languages streamline this pipeline.
Speaker 2: F shark uses the greater then greater than operator, allowing
Speaker 2: developers to chain functions sequentially, reading naturally from left to right.
Speaker 1: Oh, that's so much cleaner it really is.
Speaker 2: You literally write grind coffee, then the operator, then brew coffee.
Speaker 2: The output of the function on the left flows seamlessly
Speaker 2: as the input to the function on the right.
Speaker 1: Wow.
Speaker 2: You can chain dozens of transformations this way, like add
Speaker 2: four operator, multiply by three operator, subtract one.
Speaker 1: So what does this all mean? It sounds like composition
Speaker 1: is building a perfectly automated assembly line. The raw material
Speaker 1: drops in on the left, moves through each highly specialized machine,
Speaker 1: and the finished product talks out on the right. Yes, exactly,
Speaker 1: Because there are no side effects. You can pick up
Speaker 1: that entire assembly line and run fifty parallel copies of
Speaker 1: it across multiple cores and they will never jam or
Speaker 1: interfere with each other.
Speaker 2: A perfectly synchronized factory floor. Yeah, but you know, to
Speaker 2: make that assembly line truly adaptable. For real world applications,
Speaker 2: we need the second major tool, which is closures. The
Speaker 2: book defines a closure as a function that captures free
Speaker 2: variables from its surrounding environment.
Speaker 1: The example trail uses is an application downloading an image
Speaker 1: for the user interface. You trigger an asynchronous request to
Speaker 1: download a large image from a server right while that
Speaker 1: download is pending. The rest of the program continues executing,
Speaker 1: But when the image finally arrives, like a few seconds later,
Speaker 1: the internal function updates the correct UI element, even though
Speaker 1: the outer function that initiated the request finished its execution
Speaker 1: and moved on long ago.
Speaker 2: Yeah, the mechanism behind this is brilliant. In traditional programming,
Speaker 2: you would have to write all this complex boilerplate code
Speaker 2: to store that UI state in a shared memory heap,
Speaker 2: keep it safe from other threads, and pass it back
Speaker 2: when the network call returned, which is a nightmare exactly.
Speaker 2: A closure eliminates that overhead entirely.
Speaker 1: Explain exactly how it works under the hood. How does
Speaker 1: the function hold on to that specific data without dumping
Speaker 1: it into shared memory.
Speaker 2: Well, instead of storing the UI state in when the
Speaker 2: closure is defined, the runtime compiler automatically hoists those local
Speaker 2: variables into a hidden generated class behind the scenes. It
Speaker 2: basically takes a pristine snapshot of the environment exactly as
Speaker 2: it existed when the function was invoked. Wow, and that
Speaker 2: data lives precisely as long as the function needs it to,
Speaker 2: entirely isolated from the rest of the application.
Speaker 1: So it sounds like if Composition is building a perfectly
Speaker 1: automated assembly line, closures are giving each individual worker on
Speaker 1: that line a sealed backpack containing only the specific instructions
Speaker 1: data they need.
Speaker 2: That's a great way to look at it.
Speaker 1: They get to carry that backpack around with them no
Speaker 1: matter where they are routed in the factory or how
Speaker 1: long their specific task takes to complete.
Speaker 2: Yeah, and because the compiler and the runtime automatically managed
Speaker 2: this isolated memory context via closures, the developer is completely
Speaker 2: freed from writing bloated state management code, which is amazing.
Speaker 2: It keeps the concurrent code succinct, incredibly readable, and mathematically
Speaker 2: safe from corruption.
Speaker 1: We have covered incredible ground today. We started by staring
Speaker 1: down the immovable physics of the universe, the speed of
Speaker 1: light and the thermal melting point of silicon, which forced
Speaker 1: hardware engineers to build outward instead of upward ushering in
Speaker 1: the multi coore era right.
Speaker 2: Which subsequently exposed the fatal flaws of traditional object oriented
Speaker 2: programming definitely, we navigated the chaotic concurrency hazards of race conditions,
Speaker 2: corrupted data, and application freezing deadlocks caused by sharing mutable
Speaker 2: state across independent threads.
Speaker 1: We analyze the quick sort benchmark, proving that blindly throwing
Speaker 1: threads at a parallel problem just drowns the CPU in
Speaker 1: administrative overhead, highlighting the strict need to balance the workload.
Speaker 2: Which ultimately led us to the mathematically pure architecture of
Speaker 2: functional programming. Yes, by embracing immutability, pure functions, compositional pipelines, enclosures,
Speaker 2: developers have a robust framework to write software that scales
Speaker 2: fluidly across as many cores as the hardware can supply.
Speaker 1: So, whether you are coding the back end architecture of
Speaker 1: a mobile app, analyzing a massive big data set, or
Speaker 1: honestly just expecting your browser to remain responsive with fifty
Speaker 1: separate tabs running simultaneously, this is the invisible structural shift
Speaker 1: keeping your technology from locking up.
Speaker 2: It is the absolute foundation of modern scalable computing. But
Speaker 2: if we connect this to the broader trajectory of hardware,
Speaker 2: it raises a really fascinating question. Yeah, the physical limits
Speaker 2: of silicon forced us to adopt rigid, mathematically pure software
Speaker 2: models like functional programming to safely orchestrate multiple cores. What
Speaker 2: happens to our software paradigms when we inevitably transition to
Speaker 2: quantum computing? Will developers have to adapt yet again to
Speaker 2: write algorithms that can natively process states that are literally
Speaker 2: true and false at the exact same time.
Speaker 1: Wow, that is a staggering thought to end on from
Speaker 1: the thermal ceiling of a melting CPU to the subatomic
Speaker 1: possibilities of a quantum computer. Thank you for joining us
Speaker 1: as we mapped out the invisible architecture of the devices
Speaker 1: powering our world. Keep exploring, keep questioning, and we will
Speaker 1: see you on the next deep dive into the source material.