Skip to main content

Documentation Index

Fetch the complete documentation index at: https://mintlify.com/anthropics/original_performance_takehome/llms.txt

Use this file to discover all available pages before exploring further.

​
What is VLIW?

VLIW (Very Large Instruction Word) is a parallel execution model where multiple operations execute simultaneously within a single instruction cycle. In this architecture, each “instruction” is actually an instruction bundle containing operations for multiple engines: 
# Example instruction bundle - all operations execute in parallel
instr = {
    "valu": [("*", 4, 0, 0), ("+", 8, 4, 0)],
    "load": [("load", 16, 17)]
}
This single instruction executes:
  • Two vector ALU operations in parallel
  • One load operation in parallel
All happening in the same cycle.

​
Engine Slot Limits

Each engine can execute multiple “slots” per cycle, limited by hardware constraints:
problem.py
SLOT_LIMITS = {
    "alu": 12,      # 12 scalar ALU operations per cycle
    "valu": 6,      # 6 vector ALU operations per cycle
    "load": 2,      # 2 load operations per cycle
    "store": 2,     # 2 store operations per cycle
    "flow": 1,      # 1 control flow operation per cycle
    "debug": 64,    # 64 debug operations per cycle (free)
}
The simulator enforces these limits with assertions. Exceeding a slot limit will cause a runtime error.

​
Maximizing Parallelism

To optimize performance, pack as many operations as possible into each instruction bundle: 
# Each operation in its own instruction
[
    {"alu": [("+", 0, 1, 2)]},
    {"alu": [("-", 3, 4, 5)]},
    {"alu": [("*", 6, 7, 8)]},
    {"load": [("load", 9, 10)]},
    {"load": [("load", 11, 12)]},
    {"store": [("store", 13, 14)]}
]
The well-packed version executes 6× faster!

​
What is SIMD?

SIMD (Single Instruction Multiple Data) allows one instruction to operate on multiple data elements simultaneously.

​
Vector Length

problem.py
VLEN = 8  # All vector operations work on 8 elements
Vector operations process 8 consecutive 32-bit words in scratch space: 
# Vector add: adds 8 pairs of elements in parallel
("valu", ("+", dest, a, b))

# Equivalent to 8 scalar operations:
# dest[0] = a[0] + b[0]
# dest[1] = a[1] + b[1]
# ...
# dest[7] = a[7] + b[7]

​
Vector vs Scalar Operations

Operations: Single 32-bit word operationsSlots: 12 per cycleExample:
# Multiply two scalars
("alu", ("*", dest, a, b))
# dest ← a × b (single multiplication)
Use for: Control logic, addresses, loop counters, single values
One valu operation does the work of 8 scalar operations using just 1 slot!

​
Vector Operation Examples

​
Broadcasting

Copy a scalar value to all elements of a vector:
problem.py
# vbroadcast: dest[i] = src for all i in 0..7
("valu", ("vbroadcast", dest, src))

# Implementation:
for i in range(VLEN):
    self.scratch_write[dest + i] = core.scratch[src]

​
Vector Memory Operations

Load/store 8 contiguous elements:
problem.py
# Load 8 consecutive memory locations
("load", ("vload", dest, addr))
# dest[i] = mem[addr + i] for i in 0..7

# Store 8 consecutive memory locations
("store", ("vstore", addr, src))
# mem[addr + i] = src[i] for i in 0..7
Vector load/store only supports contiguous memory access. For non-contiguous (strided or gathered) access, you must use scalar loads.

​
Fused Multiply-Add

A special high-performance operation:
problem.py
# multiply_add: dest[i] = (a[i] * b[i]) + c[i]
("valu", ("multiply_add", dest, a, b, c))

# Does work of 2 vector ops in 1 slot!

​
Instruction Packing Example

Here’s how the reference kernel builds an instruction from slots:
perf_takehome.py
class KernelBuilder:
    def build(self, slots: list[tuple[Engine, tuple]], vliw: bool = False):
        # Simple slot packing: one slot per instruction bundle
        instrs = []
        for engine, slot in slots:
            instrs.append({engine: [slot]})
        return instrs
This naive implementation creates one instruction per slot. For better performance, you should pack multiple slots into each instruction: 
# Instead of:
slots = [
    ("alu", ("+", 0, 1, 2)),
    ("alu", ("-", 3, 4, 5)),
    ("load", ("load", 6, 7))
]
instructions = [{"alu": [("+", 0, 1, 2)]},
                {"alu": [("-", 3, 4, 5)]},
                {"load": [("load", 6, 7)]}]

# Do this:
instructions = [{
    "alu": [
        ("+", 0, 1, 2),
        ("-", 3, 4, 5)
    ],
    "load": [("load", 6, 7)]
}]

​
Performance Implications

​
Throughput Comparison

Operation TypeSlots/CycleElements/SlotThroughput
Scalar ALU12112 ops/cycle
Vector ALU6848 ops/cycle
Scalar Load212 loads/cycle
Vector Load2816 loads/cycle
Vector operations provide 4× throughput for data-parallel work!

​
When to Use Each

  • Computing memory addresses
  • Loop counters and conditions
  • Branch decisions
  • Single values that don’t have parallel analogs
  • Operations that can’t be vectorized (dependencies)
  • Processing batches of independent data
  • Element-wise array operations
  • Loading/storing contiguous memory blocks
  • Parallel hash computations
  • Any operation that can be expressed as “do the same thing to N items”

​
Next Steps

Memory Model

Learn about memory layout and scratch space

Instruction Set

Complete reference for all operations

Build docs developers (and LLMs) love

Get started for freeTalk to us