Speaker 1: Welcome to this deep dive learner. Today we are well,
Speaker 1: we're entirely focused on a crisis that honestly almost broke
Speaker 1: the tech world.
Speaker 2: It really did.
Speaker 1: Yeah, because you know, when we think about human progress,
Speaker 1: we're usually talking about these like slow, steady increments. But
Speaker 1: for the first sixty years of the electronic computer era,
Speaker 1: computing performance per dollar didn't just crawl, it sprinted. We
Speaker 1: are talking about a fifty five percent increase every single year, which.
Speaker 2: Is just I mean over those decades, that compounded into
Speaker 2: a one hundred billion fold increase in performance.
Speaker 1: One hundred billion fold.
Speaker 2: Yeah, it is a staggering trajectory. Yeah, and the industry,
Speaker 2: you know, they just kind of assumed that line would
Speaker 2: keep going up forever just by making single processors run
Speaker 2: faster and faster, right.
Speaker 1: Like going from a slow walk to literal warp drive.
Speaker 1: But then right around the mid two thousands, this unstoppable
Speaker 1: bullet train violently hit what engineers call a power wall.
Speaker 2: Yeah, the power wall.
Speaker 1: Because the processors they literally could not dissipate the heat
Speaker 1: they were generating.
Speaker 2: I exactly, the physics simply wouldn't allow it. As engineers
Speaker 2: packed more and more transistors into these tighter spaces and
Speaker 2: cranked up the clock speed. The electrical resistance generated massive
Speaker 2: amounts of heat just burning up oh completely. If they
Speaker 2: pumped any more speed into those single chips, the silicon
Speaker 2: would quite literally melt. We were hitting the hard thermal
Speaker 2: limits of the materials we were using.
Speaker 1: Okay, let's unpack this, because how do you keep making
Speaker 1: computers faster? If you know, making the engine any faster
Speaker 1: turns it into a tiny, incredibly expensive space heater.
Speaker 2: Right, that's the big question.
Speaker 1: And that is our mission today. We are using Eric
Speaker 1: Abenell's really fascinating book Elements of Parallel Computing as our
Speaker 1: guide here.
Speaker 2: It's a great foundational text.
Speaker 1: It really is. So we are going to be your
Speaker 1: shortcut to understanding how the tech world by pass this
Speaker 1: melting point, the mental gymnastics required to program these new
Speaker 1: machines and the algorithms that quietly power everything from your
Speaker 1: social media feeds to global weathers emulations.
Speaker 2: Yeah.
Speaker 1: So, faced with this power wall, how did the hardware
Speaker 1: industry actually pivot?
Speaker 2: Well, their solution was deceptively simple. Really, Instead of trying
Speaker 2: to build one incredibly fast processor that would overheat, they
Speaker 2: fundamentally change the architecture Okay, they lowered the individual clock
Speaker 2: speeds to manage the heat. But they started putting multiple
Speaker 2: processors or cores onto a single chip. They basically multiply
Speaker 2: the number.
Speaker 1: Of workers, which means moving away from that classic model
Speaker 1: that the source calls the von Neumann.
Speaker 2: Architecture, right, exactly.
Speaker 1: Yeah, for anyone who isn't like a computer historian, what
Speaker 1: does that old model actually look like?
Speaker 2: So the vond Nomen architecture is essentially a sequential bottleneck.
Speaker 2: In this model, the computer memory and the processor take turns,
Speaker 2: just one by one. Right. The processor fetches one instruction,
Speaker 2: processes one piece of data, and writes it back. Albano
Speaker 2: refers to this as SISD, which stands for single instruction, single.
Speaker 1: Data, Single instruction, single data.
Speaker 2: Yeah. It executes one single stream of instructions on one
Speaker 2: single stream of data, step by painstaking step.
Speaker 1: Okay, I think an analogy might help visualize this. If
Speaker 1: we think of SSD, that single instruction single data model,
Speaker 1: it's like having one hypercaffeinated chef in a kitchen trying
Speaker 1: to cook a massive, I don't know, one hundred course
Speaker 1: meal entirely by themselves.
Speaker 2: That's a very stressful kitchen.
Speaker 1: Oh, absolutely, they chop one carrot, and they stir one pot,
Speaker 1: then they plate one dish. It doesn't matter how unbelievably
Speaker 1: fast their hands move. They are a bottleneck.
Speaker 2: They are. And this is where parallel formats stepped in
Speaker 2: to replace it as the dominant paradigm. The hardware industry
Speaker 2: moved to architectures like SIMD that single instruction, multiple data
Speaker 2: and MIMD multiple instruction multiple data.
Speaker 1: So in our pitchin analogy, a SIMD single instruction multiple
Speaker 1: data is like well, expanding the kitchen and hiring one
Speaker 1: hundred chefs.
Speaker 2: Yes, but they don't just cook whatever they want. Far
Speaker 2: from it. In a SIMD architecture, they all execute the
Speaker 2: exact same command at the exact same fraction of a
Speaker 2: second in perfect locksdawn like in armies. Exactly, the head
Speaker 2: chef yells chop and one hundred knives hit one hundred
Speaker 2: cutting boards simultaneously. They're all doing the same instruction, but
Speaker 2: on their own multiple pieces of data.
Speaker 1: That sounds incredibly efficient and maybe slightly terrifying.
Speaker 2: To watch, honestly a little bit. Yeah.
Speaker 1: The source actually highlights a historical machine that proved this
Speaker 1: wasn't just theoretical, right, A computer from the nineteen eighties
Speaker 1: that took this to the absolute extreme.
Speaker 2: Oh, the Connection Machine or CM one built by thinking machines.
Speaker 2: It wasn't just a technological marvel. It was designed as
Speaker 2: a literal work of art.
Speaker 1: Yeah.
Speaker 2: It was this massive SIMDI computer housing and unbelievable sixty
Speaker 2: five hundred and thirty six individual processors.
Speaker 1: Wait, sixty five thousand.
Speaker 2: Over sixty five thousand, that is wild.
Speaker 1: The text mentions that the artist Tommic Oo'tell actually designed
Speaker 1: its physical enclosure, right he did.
Speaker 2: Yeah.
Speaker 1: She collaborated with the legendary physicist Richard Feynman to build
Speaker 1: this trans parent cubic case with blinking red LEDs. It
Speaker 1: allowed you to physically see the data flowing and the
Speaker 1: processors thinking in real time.
Speaker 2: It was stunning, and Feineman's involvement was crucial there. He
Speaker 2: used the CM one to write a program solving quantum
Speaker 2: chromodynamics problems.
Speaker 1: Okay, let's define that really quickly for the listener. That's
Speaker 1: the physics of how fundamental particles like quarks and gluons
Speaker 1: interact to hold the universe together.
Speaker 2: Right, exactly that it is insanely complex math, right, And
Speaker 2: by running those physics simulations on the CM one. Fineman
Speaker 2: proved that this massive parallel architecture wasn't just you know,
Speaker 2: a toy for early artificial intelligence research. It was a
Speaker 2: powerhouse for hard scientific computation.
Speaker 1: But having a beautifully designed kitchen with sixty five thousand
Speaker 1: chefs all chopping in the lockstep brings up a massive problem.
Speaker 1: The hardware was clearly ready to handle the heat, but
Speaker 1: human programmers were completely unprepared for this.
Speaker 2: Oh, entirely unprepared.
Speaker 1: Because if your recipe is only written for one person,
Speaker 1: having all those extra shifts does absolutely nothing.
Speaker 2: That's exactly it. The hardware shift was really only half
Speaker 2: the battle. Parallel computing requires a fundamental shift in how
Speaker 2: we approach problem solving, and this is notoriously difficult for
Speaker 2: programmers because as humans, we are taught from day one
Speaker 2: to program sequentially. We write a line of code, tell
Speaker 2: the computer to finish it, and then move to the
Speaker 2: next line, which is.
Speaker 1: Deeply ironic because our own human brains are parallel processors,
Speaker 1: absolutely are. I mean, we are breathing pumping blood, processing
Speaker 1: visual data from the room around us, and interpreting audio
Speaker 1: from this deep dive, all at the exact same time.
Speaker 1: But the second we sit at a keyboard, we get
Speaker 1: tunnel vision.
Speaker 2: We do. And to break out of that tunnel vision,
Speaker 2: programmers have to adopt what educators call a threshold concept.
Speaker 1: That's threshold.
Speaker 2: Come yeah, It's a transformative, almost painful, new mental model
Speaker 2: of how instructions execute. You have to stop visualizing a single,
Speaker 2: top down list of commands and start visualizing a net
Speaker 2: work of interconnected events. Aubnell introduces this as the task
Speaker 2: graph model.
Speaker 1: Let's break down this task graph model. Because it sounds
Speaker 1: like drawing a massive blueprint.
Speaker 2: Think of it as a web. The points on the web,
Speaker 2: or the vertices are individual tasks like chop the carrot
Speaker 2: or boil the water. The lines connecting those points the edges,
Speaker 2: represent data dependencies. A data dependency means one task absolutely
Speaker 2: cannot start until another one finishes and hands over its result.
Speaker 1: So boiling water before you put the pasta in exactly.
Speaker 1: But the source makes a really specific distinction here though,
Speaker 1: it separates real data dependencies from false dependencies. Why does
Speaker 1: that matter?
Speaker 2: Well, a real data dependency is a physical reality. Task
Speaker 2: B physically needs the answer from task A to start.
Speaker 2: Like your pasta example, you cannot bake the cake until
Speaker 2: you have.
Speaker 1: Mixed the batter right physically impossible.
Speaker 2: But a false dependency, which computer scientists sometimes call an
Speaker 2: antidependence or an output dependence, is essentially an illusion created
Speaker 2: by slot be human.
Speaker 1: Habits how so sloppy?
Speaker 2: How Well. In the early days of programming, computer memory
Speaker 2: was incredibly limited, so programmers would constantly reuse the same
Speaker 2: variable names, like calling a piece of data x just
Speaker 2: to save space.
Speaker 1: Ah.
Speaker 2: So, task A and task B might have absolutely nothing
Speaker 2: to do with each other mathematically, but because the programmer
Speaker 2: reuse the variable X for both of them, the computer
Speaker 2: thinks they are fighting over the same sticky note.
Speaker 1: Oh, so you just give them different sticky notes exactly.
Speaker 1: You rename the variables, give them unique labels, and suddenly
Speaker 1: you realize these tasks can actually run at the exact
Speaker 1: same time precisely. But wait, let me push back on
Speaker 1: this on behalf of you listening right now. Compilers the
Speaker 1: software that translates our human code into machine code. They
Speaker 1: are incredibly advanced today.
Speaker 2: Very true, So why do.
Speaker 1: We even need to learn to think in task graphs?
Speaker 1: Can't we just write normal sequential code and let a
Speaker 1: super smart compiler automatically map out the dependencies and translate
Speaker 1: it into parallel code.
Speaker 2: For us, this raises an important question, and honestly, it's
Speaker 2: a very common misconception.
Speaker 1: Really.
Speaker 2: Yeah, compilers are brilliant at finding small efficiencies. They can
Speaker 2: look at a few lines of code and maybe execute
Speaker 2: a few independent mathematical operations simultaneously. But a compiler cannot
Speaker 2: fundamentally rewrite a sequential algorithm into a parallel one.
Speaker 1: So it can optimize, but it can't invent exactly.
Speaker 2: It cannot look at your step by step recipe and
Speaker 2: magically invent a completely new workflow for one hundred chefs
Speaker 2: to cook it simultaneously. If the underlying logical structure you
Speaker 2: designed a sequential, the execution will be sequential. The compiler
Speaker 2: can't save you. Wow.
Speaker 1: Okay, So if the compiler can't write the recipe for
Speaker 1: our hundred chefs, we have to look at the master
Speaker 1: blueprints ourselves. We have to learn how to design the
Speaker 1: kitchen workflow from scratch.
Speaker 2: We do, and that process begins with the art of decomposition.
Speaker 1: Decomposition, Yeah, how do you.
Speaker 2: Take a massive computational problem and shatter it into smaller pieces?
Speaker 2: Ibitil outline two golden rules for parallel algorithm design.
Speaker 1: Here, Okay, lay them on me.
Speaker 2: Rule number one postpone thinking about the physical hardware until
Speaker 2: after the decomposition phase.
Speaker 1: Okay.
Speaker 2: When you are mapping the workflow, do not worry about
Speaker 2: whether you have four cores or four thousand cores.
Speaker 1: Because you just want to design the most logically perfect
Speaker 1: workflow independent of the physical machine exactly.
Speaker 2: And rule number two follows that logic create many independent
Speaker 2: tasks that scale up with the problem size. You want
Speaker 2: to design as many tasks as possible that absolutely do
Speaker 2: not need to communicate with each.
Speaker 1: Other, which brings us to perhaps the most delightful term
Speaker 1: in all of computer science. Embarrassingly parallel.
Speaker 2: It's a great term, or.
Speaker 1: As it's sometimes more politely called in textbooks, obviously parallel. Right.
Speaker 2: It's an elegant structural pattern. In an embarrassingly parallel problem,
Speaker 2: the tasks require zero communication none.
Speaker 1: Okay.
Speaker 2: A classic example Abenell uses is the Monte Carlo estimation
Speaker 2: of pi.
Speaker 1: Oh. Let's visualize this. Imagine you have a square target
Speaker 1: on a wall and a circle drawn perfectly inside that square.
Speaker 1: You stand back and throw thousands of darts at it,
Speaker 1: completely randomly, right, and.
Speaker 2: If you know the total number of darts thrown, and
Speaker 2: you count how many landed inside the circle versus outside.
Speaker 2: In the corners of the square, you can use the
Speaker 2: ratio of those areas to calculate the value of pie.
Speaker 2: In a computer simulation, every single dart throne is an independent.
Speaker 1: Task because dart A doesn't need to know where dart
Speaker 1: be landed precisely.
Speaker 2: You can have a million processors throw a million darts
Speaker 2: at the exact same millisecond and it scales perfectly without
Speaker 2: them ever needing to talk.
Speaker 1: That's so clean. The source also BOMs up those mind
Speaker 1: bending infinite images generalized fractals like the Mandelbrot set. Yes,
Speaker 1: every single pixel you see on the screen is calculated
Speaker 1: completely independently based on its specific coordinates, but the tax
Speaker 1: points out a really specific challenge with fractals, right, it.
Speaker 2: Does the challenge of load imbalance. With fractals, the computer
Speaker 2: is running a mathematical function over and over for each
Speaker 2: pixel to see if the mask diverges, meaning it escapes
Speaker 2: to infinity. Okay, for the dark pixels you see in
Speaker 2: the image, the math diverges very quickly. The processor finishes
Speaker 2: the math and a split second. But for the white pixels,
Speaker 2: the computer might have to run the calculation the maximum
Speaker 2: number of times, say two hundred and fifty five iterations,
Speaker 2: before it finally gives up in colors it white.
Speaker 1: Oh here's where it gets really interesting. Think about this
Speaker 1: as an analogy learner. It's like a teacher handing out
Speaker 1: one hundred pop quizzes to one hundred students.
Speaker 2: Okay, I like this.
Speaker 1: It's embarrassingly parallel because the students don't need to talk
Speaker 1: to each other to finish their own quiz. But the
Speaker 1: fractal problem is like some students getting a short five
Speaker 1: question quiz while others get a brutal fifty question quiz.
Speaker 1: Exactly the ones with five questions finish in two minutes,
Speaker 1: and then they just sit there staring at the clock
Speaker 1: twiddling their thumbs while the other students are sweating for
Speaker 1: an hour.
Speaker 2: That is the perfect illustration of load imbalance. Even if
Speaker 2: tasks are independent, if they take vastly different amounts of
Speaker 2: time to complete, your overall ex acution time is bottlenecked
Speaker 2: by the slowest task.
Speaker 1: So how do you fix it?
Speaker 2: To fix it, programmers have to implement dynamic load balancing.
Speaker 2: As soon as the fast students finish, you hand the
Speaker 2: more questions from the slow students' piles.
Speaker 1: Got it, But let's be real. Not all problems are
Speaker 1: isolated pop quizzes. Most real world problems require the shefs
Speaker 1: to pass ingredients to one another. We can't avoid communication forever,
Speaker 1: we can't.
Speaker 2: Know, and that is where we enter the realm of
Speaker 2: advanced choreography. This is where programmers use ingenious computational patterns
Speaker 2: to force parallel execution out of problems that look stubbornly
Speaker 2: fundamentally sequential.
Speaker 1: So let's look at one of the most famous patterns, reduction,
Speaker 1: which is the backbone of the map reduced algorithm.
Speaker 2: Yeah, reduction is a brilliant way to handle massive data
Speaker 2: sets when you need to combine everything into a single summary,
Speaker 2: like adding millions of numbers together or finding a single
Speaker 2: maximum value. Instead of having one core pains to makingly
Speaker 2: add a million numbers one by one, use a binary tree.
Speaker 1: Structure to make that visual. It's basically a tournament bracket.
Speaker 2: That's a great way to ground it. If you have
Speaker 2: eight numbers to add, you don't do it sequentially. You
Speaker 2: pair them off. Four cores add four pairs simultaneously. Okay,
Speaker 2: now you have four numbers. Then two cores add those
Speaker 2: into two numbers. Then one final core adds the last
Speaker 2: two for the total.
Speaker 1: So you reduce the data set logarithmically, meaning the workload
Speaker 1: gets chopped in half at every single step, just like
Speaker 1: teams getting eliminated in the.
Speaker 2: Sports bracket exactly. The book uses the classic map reduced
Speaker 2: word count example to show this. If you want to
Speaker 2: count how many times the word parallel appears in an
Speaker 2: entire library of books, you first map the task. Okay,
Speaker 2: each processor independently counts the word in a single chapter,
Speaker 2: Then you reduce you some those partial counts up the
Speaker 2: tournament bracket until you get the grand total. Makes total sense,
Speaker 2: and it's also the underlying logic for algorithms like amins clustering,
Speaker 2: which data scientists use to group millions of similar data
Speaker 2: points by finding common centers.
Speaker 1: Okay, reduction makes intuitive. You're collapsing a tree. But the
Speaker 1: next pattern obnel discusses the scan or the prefix sum
Speaker 1: is where my brain starts to hurt.
Speaker 2: A little bit. Is definitely a cognitive leap. I'll give
Speaker 2: you that.
Speaker 1: The source uses image compression, specifically run length decoding to
Speaker 1: explain this. If an image is compressed, a line of
Speaker 1: pixels might be encoded as four W three B, meaning
Speaker 1: four white pixels followed by three black pixels. Expanding four
Speaker 1: W and three B independently is easy, But to put
Speaker 1: them into the final computer memory array, the core expanding
Speaker 1: the three B needs to know exactly where to put them.
Speaker 1: It needs a prefix sum of everything that came before
Speaker 1: it to know its starting position.
Speaker 2: Yes, it needs to know that four items came before it,
Speaker 2: so it should start dropping its black pixels and index four.
Speaker 1: Right.
Speaker 2: But if you have millions of these compressed segments, how
Speaker 2: do you calculate the running total for every single item simultaneously?
Speaker 1: Yes, that's exactly my question. Okay, I get how you
Speaker 1: can expand them in parallel, But in the prefix sum.
Speaker 1: If lam at number four relies on the sum of
Speaker 1: elements one to two and three, how on earth do
Speaker 1: you calculate element four at the exact same time as
Speaker 1: element two. It feels like you need time travel to
Speaker 1: know the future sum.
Speaker 2: It really feels like magic, but it is actually solved
Speaker 2: by Blellock's algorithm. Guy Block figured out that you can
Speaker 2: use a binary tree just like our tournament bracket, but
Speaker 2: you sweep through it twice.
Speaker 1: Sweeping through it twice how does that actually work.
Speaker 2: So first you do enough sweep, just like the reduction
Speaker 2: we talked about, pairs of numbers are added together, moving
Speaker 2: up the tree until you have the grand total at
Speaker 2: the top. Okay, But then Bellock's algorithm does a downsweep.
Speaker 2: It takes those partial sums and passes them back down
Speaker 2: the branches of the tree, swapping and adding values along
Speaker 2: the way.
Speaker 1: Oh wow.
Speaker 2: By the time the data hits the bottom leaves of
Speaker 2: the tree again, every single element instantly knows the sum
Speaker 2: of everything that came before it.
Speaker 1: That is just brilliant. It avoids everyone standing in a
Speaker 1: single file line waiting for the person in front of
Speaker 1: them to finish adding.
Speaker 2: It is a beautiful algorithmic ballet. We see similar choreography
Speaker 2: in pipelines, where data moves through specialized stages like an
Speaker 2: assembly line.
Speaker 1: Right, the source mentions pipelines being used in two D
Speaker 1: fast Fourier transforms.
Speaker 2: Yes, which is a mathematical way of taking an image
Speaker 2: and breaking it down into its core frequencies used constantly
Speaker 2: in image processing.
Speaker 1: UH.
Speaker 2: With a pipeline, processors can calculate all the rows of
Speaker 2: an image simultaneously, past the entire block to the next
Speaker 2: stage of the assembly line and then calculate all the
Speaker 2: columns simultaneously.
Speaker 1: And Aubinell also brings up cellular automata like Conway's Game
Speaker 1: of Life.
Speaker 2: Yeah, the mechanism there is fascinating. It's a massive grid
Speaker 2: of cells that live or die based on how many
Speaker 2: neighbors they have. Instead of a processor checking roll one
Speaker 2: than row two, you assign processors to sections of the grid. Okay,
Speaker 2: every single cell checks its neighbors and calculates its life
Speaker 2: or death status simultaneously in the exact same tick of
Speaker 2: the clock. We also see this decomposition using a technique
Speaker 2: cold pointer jumping to parallelize searching through linkedless of data.
Speaker 1: You know, I am just marveling at how pristine this
Speaker 1: choreography is Blollock's algorithm cellular automoto. It's mathematically perfect on paper,
Speaker 1: it is.
Speaker 2: But having a perfect blueprint on paper is very different
Speaker 2: from building a house.
Speaker 1: True.
Speaker 2: The reality is when that pristine task graph collides with
Speaker 2: the messy physical architecture of actual computer hardware, things get complicated.
Speaker 1: Ah right. The kitchen isn't an abstract mathematical space. It
Speaker 1: has walls, limited counterspace, and physical chefs who bump into
Speaker 1: each other and drop.
Speaker 2: Things exactly In a real computer, this beautiful choreography is
Speaker 2: constantly on the verge of total chaotic collapse, and managing
Speaker 2: that physical chaos is the true art of parallel.
Speaker 1: Programming, which brings us to the messiest parts of the kitchen.
Speaker 1: You mentioned load balancing earlier. How execution time is bottlenecked
Speaker 1: by the slowest core, But the source details even more
Speaker 1: insidious physical constraints, like the dreaded data.
Speaker 2: RaSE, oh, data races. They are the absolute nightmare of
Speaker 2: shared memory systems. Imagine you have thread zero and thread one.
Speaker 2: They are both assigned to read a variable called sum,
Speaker 2: add the number one to it, and store it back
Speaker 2: in memory. Sounds simple enough at a high level, yes, yeah,
Speaker 2: But at the microscopic machine instruction level, that simple addition
Speaker 2: is actually three separate physical steps. Load the data from memory,
Speaker 2: add one to it, and store it back.
Speaker 1: Okay, load ad store Right.
Speaker 2: If thread zero and thread one attempt to do this
Speaker 2: at the exact same millisecond, their physical instructions can overlap.
Speaker 2: Thread zero loads the value let's say it's ten ten,
Speaker 2: got it before thread zero can finish adding and storing.
Speaker 2: Thread one also loads the value which is still ten.
Speaker 2: Oh no, they both independently add one, they both get eleven,
Speaker 2: and they both store eleven. The final sum should be twelve,
Speaker 2: but because of the race condition, account was lost entirely.
Speaker 1: Wow. So what does this all mean. Let's use a
Speaker 1: real world scenario. It means it's exactly like two people
Speaker 1: who share a joint bank account going to two different
Speaker 1: ATMs at the exact same millisecond. Yes, they both check
Speaker 1: the balance one hundred dollars, they both withdraw one hundred
Speaker 1: dollars if the bank's computers don't force them into a sequence.
Speaker 1: If they don't lock the account while one transaction finishes,
Speaker 1: the system glitches and hands out free money.
Speaker 2: Right. Or, in the case of scientific computing, it produces silent, invisible,
Speaker 2: completely unpredictable errors in your math. Yeah. To prevent that
Speaker 2: free money glitch, programmers have to use synchronization techniques like
Speaker 2: placing literal locks on the data. Okay, but if you
Speaker 2: use too many locks, your expensive parallel computer just turns
Speaker 2: back into a very slow sequential computer because all the
Speaker 2: processors are waiting in line for the lock to open. Right.
Speaker 1: It's a delicate balance, and the hardware can trick you
Speaker 1: in other ways too, Right, the text goes into cash
Speaker 1: coherence and fallse sharing. Let's break down how that mechanism works.
Speaker 2: Sure, so, every single processor core has its own tiny,
Speaker 2: incredibly fast memory called a cash Think of it like
Speaker 2: a mini fridge right next to a chef's prep station.
Speaker 1: Okay, I like that.
Speaker 2: When a core needs data from the main memory, it
Speaker 2: doesn't just grab one ingredient. It grabs an entire block
Speaker 2: of data, an entire shelf, and puts it in its
Speaker 2: many fridge.
Speaker 1: That makes sense, it saves trips to the main pantry.
Speaker 1: But what happens if Core A is working on variable
Speaker 1: X and Core B is working on variable Y, and
Speaker 1: they are totally independent tasks.
Speaker 2: Well, they should be embarrassingly parallel. But if variable X
Speaker 2: and variable Y happened to sit next to each other
Speaker 2: on the exact same physic, same cash block block, the
Speaker 2: hardware gets confused.
Speaker 1: Oh, because it's the same shelf exactly.
Speaker 2: When Core A modifies as variable, the computer thinks the
Speaker 2: entire shelf is contaminated. It forcefully locks korb out, passing
Speaker 2: the shells back and forth between the mini fridges, dragging
Speaker 2: performance to a total crawl. Wow, that's false. Sharing. The
Speaker 2: cores are fighting over the physical container, not the data itself.
Speaker 1: That is maddening. You write perfect, mathematically sound parallel code
Speaker 1: and the physical location of the data in the silicon
Speaker 1: trips you up.
Speaker 2: It really does.
Speaker 1: And speaking of hardware tripping you out, we have to
Speaker 1: mention SIMD control divergence.
Speaker 2: Right going back to our one hundred chefs chopping in
Speaker 2: lockstep into SIMD architecture. What happens if you have an
Speaker 2: ifel statement in your code, like what say, if the
Speaker 2: number is positive, multiplied by two, else if it's negative,
Speaker 2: divide by two.
Speaker 1: Well, the chefs can only do one command at a time.
Speaker 1: They can't do two different math equations simultaneously, exactly.
Speaker 2: Because simdcres must execute the exact same instruction at the
Speaker 2: same time. The processors have to serialize the branches serialize them. Yeah,
Speaker 2: the hardware actually forces the cores with negative numbers to
Speaker 2: go to sleep while the IF cores do their multiplication.
Speaker 2: You're kid Nope, Then it wakes up the LS cores
Speaker 2: and puts the IF course to sleep so the second
Speaker 2: group can do their division.
Speaker 1: So you just cut your processing power and have immediately
Speaker 1: just by asking a simple yes or no question, in
Speaker 1: your code.
Speaker 2: You did. And this is why parallel programming isn't just typing.
Speaker 2: It really is an art form. It's this constant, precarious
Speaker 2: balancing act between finding infinite independent tasks in your algorithm
Speaker 2: and navigating the harsh physical constraints of memory blocks, timing,
Speaker 2: and hardware quirks.
Speaker 1: Wow, Okay, let's synthesize this journey we've been on. We
Speaker 1: started with the physical crisis of the mid two thousands,
Speaker 1: those overheating single processors violently hitting the power wall. That
Speaker 1: crisis forced a hardware shift from sequential Von Norman machines
Speaker 1: to the dazzling, massively parallel art of the Connection machine
Speaker 1: and the multi court ships we use today, which.
Speaker 2: In turn forced a massive software revolution. Programmers had to
Speaker 2: abandon sequential human instincts, overcome the threshold concept, and learned
Speaker 2: to see the world as an interconnected task graph right.
Speaker 1: We explored the beautiful choreography required to make that happen,
Speaker 1: from the independent darts at the Monte Carlo estimation to
Speaker 1: the logarithmic tournament brackets of reduction and the elegant up
Speaker 1: and down sweeps of Blelock's algorithm.
Speaker 2: Beautiful stuff.
Speaker 1: And finally we grounded it all in the chaotic physical
Speaker 1: reality of data rases, cashlocks, and sleeping processors. We learned
Speaker 1: that the secret to ultimate speed isn't just building a
Speaker 1: faster engine, it's organizing a much larger team.
Speaker 2: And if you are listening to this right now, this
Speaker 2: invisible algorithmic ballet is happening all around you every day,
Speaker 2: every day. Every single time you type a query to
Speaker 2: a search engine and get millions of results in milliseconds.
Speaker 2: Every time you play a high end video game with
Speaker 2: real time physics or check a global weather forecast, you
Speaker 2: are directly benefiting from the concepts in Eric Aubenell's book.
Speaker 2: You are writing the wave of parallel computing.
Speaker 1: It really changes how you look at the devices in
Speaker 1: your pocket. I want to end with a thought for you,
Speaker 1: Tom all Over. Today, if the history of computing proves
Speaker 1: that hitting a physical wall just forces us to innovate,
Speaker 1: to divide and conquer and rethink our architecture, look at
Speaker 1: human society today. We are dealing with unprecedented information overload.
Speaker 1: We face complex, interwoven global problems that no single sequential
Speaker 1: human brain could ever possibly solved on its own.
Speaker 2: You simply don't have the individual processing speed departs at all.
Speaker 1: Exactly are we as a society about to hit our
Speaker 1: own power wall? And if so, how do we rewire
Speaker 1: our own human task graphs? How do we break out
Speaker 1: of our sequential silos, map out our dependencies, and finally
Speaker 1: start thinking and solving in parallel.
Speaker 2: And it's a great question.
Speaker 1: Think about that the next time you find yourself stuck
Speaker 1: on a massive problem. We want to thank you for
Speaker 1: joining us today and encourage you to keep diving deep