[Mono AOT] Improve `dn_simdhash` lookup on arm64

[kotlarmilos]

Description

The current implementation for arm64 in the .NET runtime isn’t optimized. Since arm64 lacks a direct intrinsic equivalent to _mm_movemask_epi8, an emulation is used, which negatively impacts performance:

runtime/src/native/containers/dn-simdhash-arch.h

Lines 93 to 124 in 367cf39

	// returns an index in range 0-13 on match, 14-32 if no match
	static DN_FORCEINLINE(uint32_t)
	find_first_matching_suffix_simd (
	dn_simdhash_search_vector needle,
	// Only used by the vectorized implementations; discarded by scalar.
	dn_simdhash_suffixes haystack
	) {
	#if defined(__wasm_simd128__)
	return ctz(wasm_i8x16_bitmask(wasm_i8x16_eq(needle.vec, haystack.vec)));
	#elif defined(_M_AMD64) || defined(_M_X64) || (_M_IX86_FP == 2) || defined(__SSE2__)
	return ctz(_mm_movemask_epi8(_mm_cmpeq_epi8(needle.m128, haystack.m128)));
	#elif defined(__ARM_NEON)
	dn_simdhash_suffixes match_vector;
	// Completely untested.
	static const dn_simdhash_suffixes byte_mask = {
	{ 1, 2, 4, 8, 16, 32, 64, 128, 1, 2, 4, 8, 16, 32, 64, 128 }
	};
	union {
	uint8_t b[4];
	uint32_t u;
	} msb;
	match_vector.vec = vceqq_u8(needle.vec, haystack.vec);
	dn_simdhash_suffixes masked;
	masked.vec = vandq_u8(match_vector.vec, byte_mask.vec);
	msb.b[0] = vaddv_u8(vget_low_u8(masked.vec));
	msb.b[1] = vaddv_u8(vget_high_u8(masked.vec));
	return ctz(msb.u);
	#else
	dn_simdhash_assert(!"Scalar fallback should be in use here");
	return 32;
	#endif
	}

This optimization can improve AOT compilation (build time) on macOS-arm64 host of MAUI template app in debug config by ~80%:

• SIMD emulation implementation:
  • AOT compilation of the dedup assembly: 247,423 ms
  • Isolated lookup for 1,000,000 iterations: 172 ms
• Software lookup implementation (g_hash_table_lookup):
  • AOT compilation the dedup assembly: 47,692 ms
  • Isolated lookup for 1,000,000 iterations: 66 ms

Alternative implementations

• https://github.com/f4exb/cm256cc/blob/master/sse2neon.h
• https://community.arm.com/arm-community-blogs/b/servers-and-cloud-computing-blog/posts/porting-x86-vector-bitmask-optimizations-to-arm-neon

Tasks

• [release/9.0-staging] [mono] Switch generic instance cache back to GHashTable; improve ginst hash function #113316
• [mono] Switch generic instance cache back to GHashTable; improve ginst hash function #113287
• [mono] Switch simdhash to prime bucket counts; increase load factor #113274

[kotlarmilos]

@kg Could we get reasonable performance on arm64 using the existing intrinsics or do you suggest using a software fallback?

[kg]

The optimized scalar search in simdhash might be better than anything we can do with neon, but I can look into the alternative methods. I don't have access to "real" arm 64 hw for testing this though, is a VM in the cloud accurate enough? I'm guessing we care about Mac or iOS here more?

…

On Mon, Mar 3, 2025, 04:49 Milos Kotlar ***@***.***> wrote: @kg <https://github.com/kg> Could we get reasonable performance on arm64 using the existing intrinsics or do you suggest using a software fallback? — Reply to this email directly, view it on GitHub <#113074 (comment)> or unsubscribe <https://github.com/notifications/unsubscribe-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ou are receiving this email because you were mentioned. Triage notifications on the go with GitHub Mobile for iOS <https://apps.apple.com/app/apple-store/id1477376905?ct=notification-email&mt=8&pt=524675> or Android <https://play.google.com/store/apps/details?id=com.github.android&referrer=utm_campaign%3Dnotification-email%26utm_medium%3Demail%26utm_source%3Dgithub> .

[kotlarmilos]

I don't have access to "real" arm 64 hw for testing this though, is a VM in the cloud accurate enough?

I think so. I can help with testing, ping me directly.

I'm guessing we care about Mac or iOS here more?

Both, macOS for AOT compilation and Apple mobile for the runtime.

[jkotas]

Tests on a macOS-arm64 host of a MAUI template app using debug config with perf checkpoints

I am not sure how to interpret these numbers. How do they translate into something user observable? I am looking for a statement like: This optimization can improve <some customer observable metric - is it app startup time?> by <how much?>.

[kg]

Tests on a macOS-arm64 host of a MAUI template app using debug config with perf checkpoints

I am not sure how to interpret these numbers. How do they translate into something user observable? I am looking for a statement like: This optimization can improve <some customer observable metric - is it app startup time?> by <how much?>.

fwiw an old Blazor startup profile I had lying around showed 18% of total CPU samples inside of hashing operations (both simdhash and ghashtable), the mono runtime's startup path spends a lot of time doing hashtable ops. different from kotlar's test workload but might be helpful.

As a result when simdhash was introduced it was a significant improvement to browser startup time, and according to measurements someone else took android had improved too? I'm guessing there's significant variance in how fast the neon version is depending on which manufacturer's ARM64 chip you're using.

[kotlarmilos]

I am not sure how to interpret these numbers. How do they translate into something user observable? I am looking for a statement like: This optimization can improve <some customer observable metric - is it app startup time?> by <how much?>.

Sorry for not stating it explicitly, updated the description. This optimization can improve AOT compilation (build time) of the dedup assembly on macOS-arm64 host of MAUI template app in debug config by ~80%. It will also likely improve execution performance on arm64 arch.