## Notes

Everything is bits!!

### 1: The Big Picture

concepts that map to one instruction fall on the “hardware” side of the hardware/software boundary arithmetic: signed/unsigned additon, subtraction, multiplication, division bitwize operations: and, or, xor etc read/write (load/store) of register values (words) to/from main memory cpu makers continue to add new low level instructions to their hardware: concepts that decompose into many carefully arranged instructions fall on the “software” side objects, arrays, methods, subroutines, functions

Jon Carmack has a good heuristic for how much stuff a CPU supports doing natively hardware: how long would it take you to write an emulator for it in software? Another good heuristic is to weigh the size of the manuals :)

binary encoding

the contents of registers, main memory, files (and the disk in general) is always bit patterns. or put another way: nothing isn’t a bit pattern the bit patterns only “mean” something in some context a good example here is that output byte stream of the shell is hooked up to the terminal, which is always interpreting bytes as teleprinter instructions so if some program is not generating binary data that corresponds to values in the ascii table, and you feed that programs output to a terminal (which is expecting bytes that do) you will get nonsense (question marks, blanks, maybe the bell will ring)

the nybble to bit pattern table

----  : 0       0000 = 00
---+  : 1       0001 = 01
--+-  : 2       0010 = 02
--++  : 3       0011 = 03
-+--  : 4       0100 = 04
-+-+  : 5       0101 = 05
-++-  : 6       0110 = 06
-+++  : 7       0111 = 07
+---  : 8       1000 = 08
+--+  : 9       1001 = 09
+-+-  : a       1010 = 10
+-++  : b       1011 = 11
++--  : c       1100 = 12
++-+  : d       1101 = 13
+++-  : e       1110 = 14
++++  : f       1111 = 15


some programs related to last night’s exercises

plain linear bitdump. we’re basically asking for no formatting, just the raw bitstream

ruby -e '
puts(            # print argument bytes to stdout, followed by newline
byte
ARGF            # file/stdin stream
.read          # byte contents of stream (as ruby string type)
.unpack("B*")  # convert bytes of string to binary string
representations
)
' <<< 'hello world' # feed ascii bytes for hello world (plus newline)
as input on stdin
011010000110010101101100011011000110111100100000011101110110111101110010011011000110010000001010


obviously that’s very hard for our wetware to interpret which is why we hexdump

$cat > hexdump.rb <<<' # save string in single quotes to hexdump.rb ARGF.each_byte .map{|b|b.to_s(16).rjust(2,"0")} # byte as hex digits .each.with_index # include indexes .each_slice(4) # groups of 4 .each{|s| send :puts, # print line with: s[0][1].to_s.rjust(6) + # first byte index, padded " " + # two spaces s.map{|b,_|b}.join(" ") # bytes, joined with spaces } ' && ruby hexdump.rb <<< 'hello world' 0 68 65 6c 6c <- bytes at index zero 4 6f 20 77 6f <- bytes starting at index 4 8 72 6c 64 0a <- bytes starting at index 8  and while we’re trying to memorize those nybble to bit pattern correspondences it’s useful to bitdump cat > bitdump.rb <<< ARGF.each_byte .map{|b| b.to_s(2).rjust(8,"0") # eight ones and zeros .gsub("1","+").gsub("0","-") # convert to +/- } .each.with_index # indexes .each_slice(4) # groups of 4 .each{|s| send :puts, # print line with s[0][1].to_s.rjust(6) + # byte index " " + # two spaces s.map{|b,_|b}.join(" ") # bytes separated with spaces } && ruby bitdump.rb x 0 -++-+--- -++--+-+ -++-++-- -++-++-- 4 -++-++++ --+----- -+++-+++ -++-++++ 8 -+++--+- -++-++-- -++--+-- ----+-+-  hexload is useful for feeding raw bytes that don’t correspond to any keys on the keyboard straight into the terminal $ cat > hexload.rb <<<
.split(/\s/)          # split on whitespace
.map{|s|s.to_i(16)}   # convert from hex strings to ints
.pack("C\*")           # convert int array into ascii string

$ruby hexload.rb <<< '68 65 6c 6c 6f 20 77 6f 72 6c 64 0a' hello world  remember, you can get to the ascii table with man ascii test your twos-compliment conversion skills online: http://www.free-test-online.com/binary/two_complement.htm endianess remember “LLL” (triple L): IF the byte with the (L)owest index (i.e. the “(L)eftmost”) is the (L)east significant byte THEN the byte order is (L)ittle endian otherwise it must be big endian you can’t tell endianness by looking at just the bytes, someone has to say “these 4 bytes 50 d6 12 00 encode the number 1234512 and then you can say “oh it’s little endian” ### 2: Overview of C (jk) • Bit fields are sets of bits where each position means something • Why would you condense stuff into bit fields? Because MEMORY ACCESS IS THE MOST EXPENSIVE THING A CPU CAN DO and you’d reduce how often this happens • 6 bitwise ops • XOR NOT AND OR SLL (shift left logical) SRL (shift right logical) • Floats are crazy • Bit field with 1 sign bit, a set of exponent bits, and an unsigned part • Arithmetic gets crazy since floats are crazy • Implemented by multiplying the unsigned by the exponent (which is two’s complement so can be negative) and then applying the sign bit • Better to use decimals that don’t repeat by default instead of floats by default • Unicode! Because ASCII always had a leading 0 and didn’t have enough space for all the characters (only 255) • Good thing they did is assign every character (not just English letters) a code. UNICODE IS A CHARACTER SET UTF-8 IS A SET OF BYTE ENCODINGS FOR THOSE CHARACTERS • Bad thing they did is come up with a new 2 byte encoding.. 2 bytes = 64,000 possibilities. Not enough! • Still use Unicode mapping of characters to numbers, but for byte encoding we use UTF-8 • UTF-8 pattern: if starts with 110, then follows ONE AND ONLY ONE byte that MUST start with 10. If starts with 1110, then two bytes follow that start with 10 • To print out a binary file: use strings a.out • xxd hexdumps a file • “magic number” at the beginning of every binary file signifies the file/encoding type • Bunch of shit at the top of the file, meant for the kernel. Mach-O headers, then load commands (instructions for kernel to prep for the program) ### 3: MIPS • Stored programs are separate from the memory. Not in use rn rly, most stuff is loaded from disk into memory • When your program runs the OS allocates space for ya • CPU and OS collaborate on how to organize memory • Segments of memory • Stack: contains stuff like function calls, local variables, etc. • Text (contains the contents of the running program. Most OS’s don’t let you write here) • Data (global constant, declare data) • Kernel (stuff that the program/OS can interact with close to metal) • I/O: constant • Heap: Grows as you ask the OS for more memory. How big is you heap? As big as you’ve asked for as your program is running • Heap grows down • malloc adds heap memory, free removes heap memory • Jumps are short (relative bytes) or long (specific address) • Special pointers • Stack pointer is end of stack (stack grows up) • Instruction pointer is next instruction (usually in text but can go to heap) • Global pointer before data segment • Heap pointer pointer to next spot you can add more heap to • MIPS is big endian • Instruction set • add/sub/div • jump • load/store • and/or/shift • You can copy by adding to 0b00000000 • In MIPS r0 is 0 • RISC vs CISC: reduced vs complex instruction sets • MIPS command • First 5 bits is the op code (only 32 ops!) • Register command, immediate command, jump command have different stuff following op code #### Berkeley Lecture Videos • https://www.youtube.com/watch?v=zUYCZYKaUrk • Instructions are primtive ops CPU may execute • Early on, adding more instructions to instruction set. Helps with vendor lock-in • Led to RISC to address bloated CPUs; keep instruction set small and make it fast • Leave it up to software to do complicated ops • MIPS is the company that built a commercial RISC architecture • “Variables” in assembly are registers • Supah fast (< 1ns access) • 32 registers because of goldilocks cool • Each 32-bit register is a word • Each register got a name, use names! • Immediates are numerical constants :) 1, 20, 30, 1512, etc. • addi to add immediate •$zero gets its own register
• Overflows yikes
• addu, subu, etc. do not detect overflow
• Store extra data in memory
• Memory addresses are in bytes
• Always fetch a whole word from memory (lw is load word)
• Before parens is the offset; in an array, lw $t0, 12($s3) gets index 3 (12 is 4 * 3 which offsets you by 3 indices)
• lb “sign-extends” a byte by putting the index 7 bit in the rest ofthe empty space. lbu does not sign-extend
• Registers are 100-500 times faster than memory
• Shift right arithmetic (sra) preserves sign to be shifted (fills in 1s to the left side)
• Branching for conditionals in MIPS: beq register1, register2, label
• Unconditional branch is a jump (j)
• Computer words are instructions, vocabulary is instruction set
• Assembly code is assembled into object files, which are “linked” to machine code executable files
• VMM (virtual memory manager) built into CPU for managing virtual memory space efficiently
• To branch on equality, use bne and go to Exit label if u dead
• slt reg1, reg2, reg3
• Set Less Than
• reg2 < reg3 ? reg1 = 1 : reg1 = 0
• slti uses immediate
• Fundamental steps in calling a function
• These all happen in high level programs but we don’t think about them as much
• Put paramteres where function can access them
• Transfer control to function
• Acquire resources needed for function
• Put result somewhere calling code can find it, restore stack registers
• Return control to point of origin (jump back)
• In MIPS:
• $a* registers are for arguments •$v* registers are for value registers to return
• $ra is return address to hop back to • jal is to jump and link! • So, caller puts params in registers$a0-$a3 then uses jal X to invoke X • jal puts the address of the next instruction into$ra
• To save old register values after function call ($s* registers), you gotta save those somewhere and restore after • Use a stack! • Grow from high to low address; so push decrements$sp (stack pointer), pop increments it
• MIPS only tells you to save $s0 to$s7
• use sw to store stuff in the stack before your function, then lw to bring them back after

#### Pre-work

• Translate vs. interpret
• High-level languages are interpreted, which means they are executed by another program
• Low level languages are translated into an intermediate step
• Interpreting 10-100x slower
• C is compiled :). Let’s compile foo.c
• foo.c
• Through compiler
• Assembly program: foo.s
• Through assembler
• Object (mach lang module): foo.o
• Executable (mach lang program): a.out
• Come to memory papa
• Compiler
• Input is C code, output is assembly code
• Might produce pseudoinstructions like move (add 0 and copy)
• Assembler
• Input is assembly language
• Output is object code
• Reads and uses directives (.text, .data, .asciiz, etc.)
• Expand pseudoinstructions
• E.g. no subu, so do addiu with negative value
• Multiplication: m x n = m + n bits product
• Result goes into hi and lo; hi is the upper half, lo is the lower half
• Produce machine language
• Wat do
• Simple case is just arithmetic, shifts, logic, etc. Easy
• Branches tough :( Relative to where your pc is
• Where is the label you want to jump to? Solved by taking two passes over the program, once to remember position and other using those label positions to generate code
• Object file
• object file header: size and position of other stuff in file
• text segment: machine code
• data segment: binary static data
• relocation info: identifies lines of code to be fixed up (i.e. include directive)
• symbol table: list of labels and static data references
• debugging info
• Standard format is ELF
• Combines a ton of .o files to make one a.out file
• Input is .o files
• Output is executable code
• Combines several files into a single executable
• Enables separate compilation of stuff, so you can change and recompile one file without doing the whole project
• Takes the text/data/info from multiple o files and then sticks them together interleaved with each other
• Resolves references in files. Go thru Relocation Table, look at each entry, and repace with absolute address
• PC-relative address (beq, bne) never relocated
• Absolute Function address (j, jal), External Function reference (jal), Static Data reference (lui, ori) always relocated
• We assume the first word of first text segment is at 0x04000000 (stuff below is reserved)
• We know how long each text and data segment is, and how they are ordered
• So we calculate the absolute address of each label and each piece of data being referenced after concatting
• Resolve references:
• Search for reference in symbol tables
• if not there, search lib files
• Finally, fill in correct machine code
• Final output is machine executable file with header
• Takes executable code and runs it
• Executable code is on disk, loader needs to load into memory and run
• Assign amount of memory for each piece - text, data, stack
• Copies instruction and data into address space
• Copies arguments onto stack
• Initializes machine registers to be usado
• Jumps to start-up routine that copies program’s arguments from stack to registers and sets the program counter
• This naive approach is statically-linked. We bring in the entire library even if not all is used
• Alternative is dynamically linking, which is common oon UNIX and stuff

#### Building Mach O executable

• https://www.mikeash.com/pyblog/friday-qa-2012-11-30-lets-build-a-mach-o-executable.html
• PAGEZERO load command blocks off lower 4GB of memory space, so that dereferencing NULL pointers causes a segmentation fault
• What means? This is the __PAGEZERO segment, which predefines the entire lower 4GB of the 64-bit virtual memory space as inaccessible. Because of this segment, which is marked unreadable, unwriteable, and nonexecutable, dereferencing NULL pointers causes an immediate segmentation fault.
• Load Commands - it’s kind of a table of contents, that describes position of segments, symbol table, dynamic symbol table etc. Each load command includes a meta-information, such as type of command, its name, position in a binary and so on.

#### Lecture

• Compiling a C file goes a long ways…
• cc is just a frontend for clang and ld (linker)
• See compiling_step_by_step.sh for deets

### 5: The Processor, Clock, and Datapath

#### Prework video

• Don’t need much to run software in hardware!
• Take physical device and run programs on it? How?
• Start with truth table, minimize and implement as we’ve seen before
• Solve the subproblem! Add 1 bit before thinking about 32 bit
• Ok so let’s think about instructions for adding 1 bit 3 times
• Sum of three bits is XOR(a, b, c) (with three inputs, odd number of 1s is a 1, even number of 1s is a 0)
• The carry is MAJ(a, b, c) which is a&b + a&c + b&c (the sum of these)
• But you need to worry about overflow
• If last carry bit is 1, you have overflow
• To do subtractor, take first number and add to negative of second number
• Negative done by two’s complement
• Add another bit input to adder to designate whether second input should be flipped
• AN XOR SERVES AS A CONDITIONAL INVERTER CAUSE YOU CAN INVERT EACH BIT
• THEN TAKE THE SUM AND SUBTRACT ONE AND YOU’RE GUCCI
• Components
• Processor has a control and a datapath
• Control tells datapath what to do (what registers to read, which operation to perform)
• Datapath includes PC, Registers, ALU
• Processor connects to memory
• Memory connects to I/O
• So there’s two boundaries: processor-memory interface and memory-I/O interface
• CPU
• Processor is the CPU, active part of the computer that does the work
• Datapath contains hardware to perform operations
• Control: tells the datapath what needs to be done (brains)
• How to execute instruction?
• Fetch: fetch the 32-bit instruction word from memory, returns them in the control unit, and then increment program counter
• Decode: look at 32-bit instruction and figure out what it means.
• First thing is obviously the opcode.
• Then, read data from necessary registers (2 for add, 0 for jump)
• ALU: do work like arithmetic, shift, logic. Also needs to be done for lw and sw to add the offset to the address
• Memory access: only loads and stores do stuff here. Should be fast
• To store, need to read a) base address, b) data value to store, c) immediate offset
• Register write: write result back to register
• For store instruction that writes to memory, work has been done in last stage
• Many instructions don’t use all stages!
• Lecturer walks through these steps, makes sense
• Immediate values come out of instruction memory
• Not all instructions use all stages, but for MIPS at least it’s the union of all the operations needed by all instructions
• Load instruction uses all 5 :)
• Datapath and control bois
• Datapath->control feedback is from instruction memory
• Controller makes sure right things happen at right time, can hook into all parts of datapath
• Processor design
• Analyze instruction set to determine datapath requirements. Must support everything!
• Select set of datapath components and establish clocking. Things happen on the rising edge
• Assemble datapath components
• Analyze implementation of instructions and set up control points
• Assemble control logic (formulate equations, design circuits)
• 3 types of MIPS instructions
• R type has op, register s, register t, register destination, shamt (shift amount), funct (add, subtract, etc.)
• I type has op, register source, register taret, immediate
• J type has op code then target address
• Register Transfer Langauge is a way of writing down what happens during execution of each instruction
• Pseudocode ish. For ADDU instruction, RTL is like this: R[rd] <– R[rs] + R[rt]; PC <– PC + 4
• All instructions start by fetching instruction itself
• Requirements of instruction set for our MIPS light: stuff like MEM, Registers, PC, sign extender, ALU, PC incrementer, etc.
• So now for our components. Need combinatorial elements (don’t respond to clock) and storage/sequential elements (respond to clock)
• Describes his class’s architecture cool
• Clock stuff
• “Critical path” (longest path through logic) determines the length of the clock period
• Art of hardware design is moving clock edges closer together, shortening critical path
• State machine that reads the instruction, updates state, then awaits next instruction. Cool

#### Lecture

• Hertz is number of switches per second
• Clock cycle
• Starts with rising edge with high current, then it has a down edge with lower current, then back to rising edge, ez
• To get faster, reduce critical path speed OR add flip flops/registers in the middle to save work
• All digital systems with time:
• Current state sent from stateful part to combinatorial logic along with static inputs
• Combinatorial logic does work and emits outputs, and next state is sent to the clocked chip
• “Next state” from previous step is the state passed in during the next clock cycle
• C Pro Tip
• Read char *argv[]. WHEN you invoke *argv, you get the other part of the expression, which is char[]

### 6: Using Logic Gates to Build Logic Gates

#### Prework video

• Why study hardware even if you don’t work on hardware? Want to understand capabilities and limitations so you can utilize hardware effectively
• Basics of a computer system is a synchronous digital system
• Synchronous: all operations coordinated by central clock
• Digital: all values are discrete value (analog = voltage, etc.)
• Binary (0, 1). Electrical signals are 1 and 0 (high and low voltage)
• Implement a circuit/switch
• If you close a switch and complete a loop, then current flows and lightbulb can be on
• Boolean logic based on Boole lol
• Transistors used to represent high/low voltage (they’re the switches in computers)
• Remove noise by setting a midpoint voltage; above is 1, below is 0
• CMOS is ours: Complementary Metal Oxide on Semiconductor
• n-type transistor is open when no voltage, closed when voltage. p-type is opposite
• Basically you can use complementary pairs to get strong signals
• https://gyazo.com/2cc4aaf3f68f6495f7646d6200068cd7
• NAND!
• Some combinatorial logic symbols are standard zzz
• Truth tables describe the inputs and outputs of a circuit
• Simplifying boolean logic is an art form rofl
• Boolean Algebra
• for OR (logical sum)
• dot for AND (logical product)
• Hat for NOT (complement/negation)
• Bunch of laws of boolean algebra
• Signals and waveform stuff
• Can look across separate wires to aggregate a signal
• Propagation delay is difference between changing input and changing output

• Synchronous digital systems help abstract time/delays
• Come with two types of circuits
• Combinatorial logic: output is a pure function of the inputs, doesn’t have history of execution
• Sequential logic: circuits that remember or store information across time. Clocks synchronize systems!
• https://gyazo.com/738b402a3fef46496bdfd7c8acc3fed6

#### Lecture

• Flip flop circuits are like camera shutters: open, snapshot, emit, close, etc

• CLOCKS ARE IMPORTANT. THEY MAKE EVERYTHING GO
• SIMD: Single Instruction Multiple Data
• Can load 4 32-bit ints into a 128-bit register, then can do four adds in parallel
• Think about flow of electricity to model voltage in circuits

• How is NAND implemented in electric circuits?

### 7: Pipelining

#### Video

• Single Cycle processor review
• See “processor design” segment
• End up with cool datapath
• Performance: for every instruction, need to wait until worst case time for worst instruction. Clock rate (cycles/second = Hz) = 1/period (seconds/cycle)
• Pipeline increases clock rate over worst case performance
• Increased clock rate means faster programs hopefully
• Can overlap the stages of stuff to make it more efficient
• Analogy is laundry (washer, dryer, folder, stasher). Sequentially takes two hours, but if you do batches with pipelining you get more efficient. After first load is washed, you load it into the dryer and immediately add second load to washer
• Pipelining does not help latency of single task, it helps throughput of entire workload
• Multiple tasks operating simultaneously using different resources
• Potential speedup = the number of pipe-able stages
• Time to “fill” pipeline and time to “drain” it reduces the speedup :)
• Pipeline limited by slowest pipeline stage (still better than being limited by the slowest entire instruction, which you are without pipelining)
• Try to balance lengths of pipe stages
• Apply pipeline to MIPS assembly
• Just add registers between stages (fetch, decode, execute, memory, write back)
• These registers hold information produced by previous cycle
• Need to keep a copy of instruction bits and move them down the pipeline so each piece knows exactly what to do with that instruction
• Several ways to represent pipeline (graphical, etc)

• Pipelining performance
• Best case is Time of single cycle / number of stages (equality is only achieved if stages are balanced)
• Speedup reduced if not equally balanced
• Remember, pipelining increases throughput not latency
• Pipelining increases instruction latency (must match longest instruction latency), does not increase number of components

• Pipelining hazards precent starting the next instruction in next clock cycle
• Structural hazard: required resource is busy (e.g. needed in multiple stages)
• e.g. Multiple registers need to be write/read simultaneously
• Easy-ish to solve
• Keep separate caches for instruction fetch and memory RW
• Split RegisterFile access in two: write during 1st half and read during 2nd half of each clock cycle
• So, read and write to registers during same clock cycle is okay
• Can always be removed by adding hardware resources
• Data hazard: data dependency between instructions, need to wait for previous instruction to finish up
• Data flow backwards is a hazard (e.g. an add happens in one instruction, a bunch of subsequent instructions need to use the value produced by that add
• Cool. Register forwarding: forward result as soon as it’s available, even if it’s not stored in RegFile yet. Add a sneak path for forwarding value from output of ALU to input of ALU without writing/reading registers or memory
• What’s the datapath for forwarding?
• Add a forwarding unit that checks source registers, compares them to registers written in earlier instructions, and if they match, then you do the forwarding. E.g. if $t0 written in one add (destination), then$t0 used in subsequent subtract (source), then mux in the new value
• Loads are tough, cause you need the memory value. Can’t forward, need to wait until load value is actually available. Must stall instruction dependent on load, and then forward
• Called hardware interlock when hardware stalls pipeline
• Replace stalled instruction with “bubble”, which is a no-op
• Slot after a load is a load delay slot, can’t use loaded value for one slot.
• Don’t try and use a value once cycle after load :) nop instead
• MIPS doesn’t have interlocked pipelining stages :)
• But adds back interlock because it’s smelly to nop everywhere
• Compiler can help with hardware interlock by inserting unrelated instruction into that space so you can take advantage of nop time
• Can save stuff
• Control hazard: Flow of execution depends on previous instruction (branch or jump)
• Branch determines flow control
• Simple solution option 1: just stall on every branch instruction until branch resolved
• Adds 2 bubbles/cycles for each branch :( (20% of instructions). Compare happens at ALU stage, so must wait till then
• Optimize: insert special branc comparator after stage 2. Can only do equality check. Chop penalty down to only one bubble
• Optimize: Predict outcome, fix up if guess wrong
• If you’re wrong, must flush pipeline
• Can predict that all branches are NOT taken; just keep going and fall through :) Only need to flush if branch ends up being taken
• Lots of effort spent on this!
• Optimize: can rearrange instructions (compiler) to fill branch delay with an unrelated, still useful instruction

### 8: Memory Hierarchy

#### Mike Acton Data-Oriented Design and C++

• On the engine team - supports the runtime systems that games are built on top of
• Don’t use templates in CPP
• Lots of language features are sad and not used for important stuff
• Data oriented design: the purpose of all programs is to transform data from one form into another
• Corollaries: if you don’t understand the data you don’t understand the problem. You understand a problem better by understanding the data. If you have different data you have a different problem. If you don’t understand the cost, you don’t understand the problem. If you don’t understand the hardware, you can’t reason about the cost of understanding the problem. Everything is a data problem. :)
• Solving problems you don’t have will add to the number of problems you do
• Where this is one, there are many. Try looking at the most common problems and stuff first.
• Software does not run in a vacuum!
• Reason must prevail!
• Data-oriented: a reminder of first principles :). Not new ideas at all
• Lies of CPP
• Software is a platform
• Hardware is the platform fam. Reality isn’t some annoying thing making your solution ugly, reality is the real problem
• Code should be designed around your mental model of the world
• Don’t hide data in your mental model! Confuses maintenance with understanding properties of data (which is critical for solving problems). Don’t try and idealize the problem
• Code more important than data
• No. Code exists to transform data. Programmer responsible for DATA not code
• Lies lead to poor performance, concurrency, optimizability, stability, testability. Oops
• Solve to transform data you have to where you want it given the constraints of the platform. Nothing else dude

• Solve for most common case first, not most generic
• “Make the compiler do it”. No. Compiler reasons about instructions, which is only 1-10% of the problem space
• Let’s look at memory hierarchy stuff. Much much more expensive to go to main memory than to have compiler optimize away expensive CPU instructions. This is an order of magnitude
• Don’t miss the cache!
• If cache line is 32 bytes let’s see

• For bools, using bool is huge cost because lots of wasted space
• Only fills 1 bit of 512 in the cache line. Can try and squeeze other stuff in as well
• Don’t reason about stuff super locally if you can do it at a higher level

• Concentrate on common case first

#### P&H 5th Edition Memory Hierarchy (5.1 - 5.4)

• Create illusion of unlimited amounts of fast memory
• If you’re writing a paper at a desk, you wanna keep the most important documents and references close by so you don’t have to keep getting up to access stuff
• Same principle here: create illusion of large memory by swapping stuff out of a small memory behind the scenes
• Principle of locality. Programs access a small portion of address space at any given time
• Temporal (in time): if something referenced, likely to be referenced again. Keep it around
• Spatial (in space): if something referenced, stuff around it is likely to be referenced soon
• Cache faster than SRAM faster than DRAM faster than disk
• S = Static
• D = Dynamic
• Take advantage of locality with a hierarchy
• Faster but smaller the closer you get to processor
• Data only copied between two adjacent levels at a time
• “Upper” = closer to processor, “Lower” = further away
• Block/line: minimum unit of information that can be present or not present. Library analogy is a book
• If present, it’s a hit. If not, it’s a miss. Hit rate is the fraction of accesses that are in the upper level; Miss rate is 1 - hit rate
• Hit time is amount of time taken for a hit. Miss penalty is time to replace block in upper level with the missed data from lower level, then deliver data to processor

• SRAM are memory arrays that keep values indefinitely as long as power is applies. DRAM stores stuff as a charge on a capacitator, must be refreshed (read and write back) periodically. Organized in rows for read and write (?)
• Hardware stuff over my head
• Flash memory is older stuff, it wears down over time so you have to use a technique called wear leveling to distribute load over the whole thing
• Magnetic hard disk supah slow. They are a series of magnetic disks connected together and moving in conjunction
• Three steps. First, seek, which moves the head to the proper disk track. Average seek times 3-13ms. Then, when head is on correct track, must wait for the right sector to rotate to the head.. Average is like 5ms. Finally, transfer time is how long it takes to move a block to the head. Transfer rates in 2012 ~ 100-200MB/sec.
• Primary diff between magnetic disk and semiconductor memory is that disks are way slower because they’re mechanical.

• Caches are memory levels between processor and main memory
• How to find where in cache something is stored? Similar to naive hashing algorithm: block address module number of blocks in cache. This is direct mapping
• Add tags to the cache data decribing where it originally lived
• How do you know if cache has valid data? (e.g. stuff can be stale after a processor startup). Add a valid bit that is on if entry has a valid address
• Caching part of prediction. Rely on principle of locality to try and find desired data, and retrieve the correct data if can’t find it in the caches. Today computers are 9% cache hit
• Using this modulo approach we’ll have block conflicts. Uhh how to resolve idk yet
• “The tag from the cache is compared against the upper portion of the address to determine whether the entry in the cache corresponds to the requested address. Because the cache has 210 (or 1024) words and a block size of one word, 10 bits are used to index the cache, leaving 32 −10 − 2 = 20 bits to be compared against the tag. If the tag and upper 20 bits of the address are equal and the valid bit is on, then the request hits in the cache, and the word is supplied to the processor. Otherwise, a miss occurs.”
• Nice diagrams buddy
• Larger block = lower miss rate b/c of spatial locality
• There’s a sweet spot b/t block size + number of blocks that fit in the cache
• Larger block also means worse miss penalty :( takes longer to load missed data into the cache
• How does processor control handle miss?
• Most of the time, just introduce a stall until missed data has been loaded (if cache hits, just proceed as normal)

• After write to memory, cache and memory are inconsistent. Solve this with write-through: write to both spots each time. But this is sucky and slow. Need to spend a lot of cycles writing to main memory synchronously
• Better? Write buffer stores data while waiting to be written to memory. Processor just needs to write to the buffer (fast), and it goes from there to main memory. If write buffer is full, processor still has to wait for free space
• Write-back also possible. When write occurs, new value only goes to cache, then written to lower levels when it is replaced. Hard to implement but better performance

• How do miss rate and execution time relate to each other? Let’s see

• How to measure and improve cache performance
• Two techniques: reduce miss rate by reducing probability that two different memory blocks dispute over the same cache spot, reduce miss penalty by adding more levels to hierarchy
• CPU time = (CPU execution clock cycles + Memory-stall clock cycles) x Clock cycle time
• Sometimes can assume hit time is just factored into a clock cycle, but if you want to get granular about that too you can use a metric called Average Memory Access Time (AMAT): time for a hit + miss rate x miss penalty

• Can you have same block in multiple locations? i.e. not direct mapped.
• Opposite of direct mapped is fully associative; any block can be anywhere. Search whole block for what you want
• Middle range is Set associative. Set number of spots where each block can be placed in n locations (descript would be “an n-way set-associative cache”)
• Calculate where block can go by (block#) % (#sets in cache)
• Must search all tags in set when looking for a block
• Tradeoff of increasing associativity: lower miss rate but increase hit time
• Definitely diminishing returns; in 64KiB with 16-word block, associativity from 1 to 2 is like a 1.7% improvement in miss rate, 2 to 3 is like 0.3%
• Searching n-associative cache for your data :thinking_face: Sequential search too slow! Instead, search them all in parallel. Need one comparator for each level of associativity you add. So cost is extra hardware + cost of doing compares

• Which block do you replace? Need to choose among blocks in set.
• Commonly just do LRU

• Multilevel cache to improve performance even more
• Primary cache usually smaller than secondary…can use smaller block size as well. Also lower associativity
• Interesting comparison between radix sort and quicksort. Radix sort algorithmically quicker, but since quicksort has fewer misses per item it can still perform better

• How to use in software?
• e.g. if you’re working on an array or list or matrix that won’t all fit into memory at the same time, instead of going row by row or column by column you want to go by block size so you can fit the entire block you’re operating on into memory at the same time. Compute block size with pointer arithmetic and only fetch what’s necessary. Muy bien

• Summary?
• Cache performance, using associativity to reduce miss rates, multilevel hierarchies to reduce miss penalties, software optimizations to improve cache usage

#### Lecture

• Registers: 1ns access, ~ 1kb, CMOS tech, managed by compiler/programmer
• L1: 3ns, 32KB, SRAM, CPU
• L2: 6ns, 256KB, SRAM, CPU
• L3: 12ns, 8MB, SRAM, CPU
• RAM: 60ns, 16B, DRAM, OS

• Half of each cache is for instructions, half for data
• Why? Different data access patterns. e.g. if iterating through array, iteration code is static (keep the instruction cache), but get all the new data (evict data cache)
• Struct packing: wise about ordering of elements in C Structs because of how compiler will organize your memory

• Misses
• Compulsory: nothing in those spots, just go fetch it
• Conflict: gotta try searching in the set
• Evict: least recently used thing tossed out to make room

### Parallelism, Flynn taxonomy, Amdahl’s law

#### Berkeley lectures (CS 61C 3-31-2015 and 4-2-2015)

• New school architecture is crazy. Parallelism :thinking_face:
• Use a whole bunch of processors to make things faster
• Two ways:
• Multiprogramming: different independent programs in parallel
• Parallel computing; run multiple at the same time on same machine. Way harder
• SIMD: single-instruction/multiple-data
• Multiple data streams against a single instruction stream. GPU stuff. Take a “pool” of data and apply same operation to all of them at the same time
• MIMD: multiple processor cores executing different instructions on different data
• MISD: not very common at all. Odd design
• SIMD/MIMD: Flynn taxonomy.
• Software: SPMD programming. Single Program Multiple Data. Run same program on different sets of data in different places
• Big idea: Amdahl’s (heartbreaking) law. Speedup due to enhancement E
• Only speedup to parallel steps, suequential steps still neeed to go sequentially
• Speedup w/E = exec time w/o E DIVIDED BY exec time w/E
• Basically, parallelization speedup is less than you’d intuit
• Strong/weak scaling: getting good speedup on parallel processor while keeping problem size fixed is hardeer than getting good speedup by increasing the size of the problem
• Strong: speedup can be achieved on parallel processor without increasing size of problem
• Example: graphics (parts of the screen aren’t dependent on others)
• Weak: speedup can be achieved on a parallel processor by increasing size of problem proportionally to increase in number of processors
• Load balancing: each processor should do just about the same amount of work! Always have to wait for slowest processor

• SIMD architecture
• Data parallelism is executing same operation on multiple data streams
• Example: multiply a coefficient array by data array (all elements)
• Sources of improvement:
• Only one fetch/decode
• All operations known to be independent
• Pipelining/concurrency in memory access
• Intel calls it Advanced Digital Media Boost -_-
• MMX: Multimedia Extensions. Used 64 bit registers that would be considered broken up. Then parallel ops could be done (1992)
• SSE: Streaming SIMD Extensions: Added 128 bit registers (1999)
• Now AVX: 256 bit registers (2011). Space for expansion to 1024 bit registers!
• Array processing in SIMD
• Without parallelism, need to load each element into float register, calculate sqrt, write result back
• With parallelism, Load 4 members into the SSE register, calculate 4 in one operation, stoe them all from register to memory
• This kinda stuff is expressed in programs as for loops
• In MIPS, this would just be a sequential set of instructions as described above “without parallelism”
• Can unroll a scalar loop to do 4 elements at a time. Only 1 loop overhead every 4 iterations. Uses different registers for each iteration to eliminate data hazards in the pipeline
• Now schedule things by doing all loads, then all adds, then all stores. Cool
• If not in multiple of 4, have a separate loop that handles odds
• SIMD this thing by just converting unrolled instructions into one SIMD instruction
• MOVAPS: move aligned, packed, single. Cool