Optimization Strategies

Techniques to reduce cycle count in VLIW SIMD architectures.

Strategy 1: Instruction Packing

Problem: Baseline puts one instruction per cycle.

Solution: Pack multiple independent operations into the same cycle.

Before (3 cycles)

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Cycle 1: { "alu": [("+", 0, 1, 2)] }
Cycle 2: { "alu": [("*", 3, 4, 5)] }
Cycle 3: { "alu": [("-", 6, 7, 8)] }

After (1 cycle)

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Cycle 1: { "alu": [("+", 0, 1, 2), ("*", 3, 4, 5), ("-", 6, 7, 8)] }

Potential speedup: Up to 12x for ALU-heavy code (12 ALU slots per cycle).

Strategy 2: Vectorization

Problem: Processing 1 item at a time when batch has many items.

Solution: Use vector operations to process 8 items simultaneously.

Before (scalar, 8 items = 8 cycles minimum)

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for i in range(8):
    ("alu", ("+", result[i], a[i], b[i]))

After (vector, 8 items = 1 cycle)

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("valu", ("+", result_vec, a_vec, b_vec))  # All 8 at once

Potential speedup: 8x for data-parallel operations.

Strategy 3: Use Loops Instead of Unrolling

Problem: Unrolling all iterations as separate instructions bloats code size.

Solution: Use cond_jump to loop, reducing code size.

Before (unrolled)

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# 4096 copies of the same instructions
body.append(("alu", ("+", ...)))  # item 0
body.append(("alu", ("+", ...)))  # item 1
body.append(("alu", ("+", ...)))  # item 2
# ... 4093 more ...

After (loop)

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# Single copy of instructions, executed 4096 times
loop_start = len(self.instrs)
body.append(("alu", ("+", ...)))       # The work
body.append(("alu", ("+", counter, counter, minus_one)))  # Decrement counter
body.append(("flow", ("cond_jump", counter, loop_start))) # Loop back

Benefits: Smaller code, fits in instruction cache, easier to reason about.

Strategy 4: Constant Caching

Problem: Loading the same constant multiple times.

Solution: Load constants once at startup, reuse from scratch.

Before

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("load", ("const", tmp, 2))  # Load 2
("alu", ("*", x, x, tmp))    # x * 2
("load", ("const", tmp, 2))  # Load 2 again!
("alu", ("*", y, y, tmp))    # y * 2

After

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# Setup (once)
("load", ("const", two_const, 2))

# Use (many times)
("alu", ("*", x, x, two_const))
("alu", ("*", y, y, two_const))

Note: Many VLIW toolchains provide helpers for constant caching.

Strategy 5: Memory Access Batching

Problem: Loading one value at a time.

Solution: Use vload/vstore to transfer 8 values at once.

Before (8 loads = 8 cycles minimum)

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for i in range(8):
    ("load", ("load", scratch[i], addr[i]))

After (1 vload = 1 cycle)

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("load", ("vload", scratch_base, addr_base))  # Loads scratch[base:base+8]

Constraint: Memory addresses must be consecutive.

Strategy 6: Pipeline Multi-Stage Computations

Problem: Multi-stage computations where each stage depends on the previous create serial bottlenecks.

Solution: While computing stage 3 for element A, compute stage 2 for element B, stage 1 for element C.

Visualization

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Cycle 1: [A-stage1]
Cycle 2: [A-stage2] [B-stage1]
Cycle 3: [A-stage3] [B-stage2] [C-stage1]
Cycle 4: [A-stage4] [B-stage3] [C-stage2] [D-stage1]
...

Complexity: High - requires careful register allocation and dependency tracking.

Strategy 7: Use Different Engines in Parallel

Problem: Only using ALU when you could also use VALU, Load, Store simultaneously.

Solution: Schedule operations across engines.

Before

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Cycle 1: { "alu": [compute1] }
Cycle 2: { "load": [load1] }
Cycle 3: { "store": [store1] }

After

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Cycle 1: { "alu": [compute1], "load": [load1], "store": [store1] }

Optimization Priority

For most VLIW SIMD workloads, the most impactful optimizations are:

1. Vectorization - Process multiple items at once (Nx speedup where N is vector length)
2. Instruction packing - Fill all engine slots
3. Loops - Reduce code size, enable other optimizations
4. Memory batching - Use vector loads/stores

The combination of vectorization + packing can achieve significant speedups over naive implementations.

Applying These Strategies

For a concrete example applying all these optimization strategies, see the Anthropic Performance Take-Home project, which demonstrates achieving 100x+ speedup on a VLIW SIMD architecture.

Data Dependencies - The Constraint

You can only pack operations that are independent:

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# CANNOT pack - b depends on a
a = x + 1
b = a * 2  # Must wait for 'a'

# CAN pack - no dependencies
a = x + 1
c = y + 1  # Independent of 'a'

Finding independent operations to pack is the core challenge of VLIW programming.