Intel Iris Plus G7 Gen12 XE TigerLake ULV (i7-1165G7) Review & Benchmarks – GPGPU Performance

What is “XE” / “TigerLake”?

It is 3rd update of the “next generation” Core (gen 11) architecture (TGL/TigerLake) from Intel the one that replaced the ageing “Skylake (SKL)” arch and its many derivatives that are still with us (“CometLake (CML)”, “RocketLake (RKL)”, etc.). It is the optimisation of the “IceLake (ICL)” arch and thus on update 10nm++ again launched for mobile ULV (U/Y) devices and perhaps for other platforms too.

While not a “revolution” like ICL was, it still contains big changes SoC: CPU, GPU, memory controller:

• 10nm++ process (lower voltage, higher performance benefits)
• Gen12 (XE-LP) graphics (up to 96 EU, similar to discrete DG1 graphics)
• DDR5 / LPDDR5 memory controller support (2 controllers, 2 channels each, 5400Mt/s)
• No eDRAM cache unfortunately (like CrystallWell and co)
• New Image Processing Unit (IPU6) up to 4K90 resolution
• New 2x Media Encoders HEVC 4K60-10b 4:4:4 & 8K30-10b 4:2:0
• PCIe 4.0 (up to 32GB/s with x16 lanes)
  

While ICL has already greatly upgraded the GP-GPU to gen 11 cores (and more than doubled to 64EU for G7), TGL upgrades them yet again to “XE”-LP gen 12 cores now all the way up to 96EUs. While again most features seem to be geared towards gaming and media (with new image processing and media encoders) – there should be a few new instructions for AI – hopefully provided by a OpenCL extension.

Again there is no FP64 support (!) while FP16 is naturally supported at 2x rate as before. BF16 should also be supported by a future driver. Int32, Int16 performance has reportedly doubled with Int8 now supported and DP4A accelerated.

The new memory controller supports DDR5 / LPDDR5 (5400Mt/s) that should – once memory becomes readily available – provide more bandwidth for the EU cores; until then LPDDR4X can clock even faster (4267Mt/s). There is no mention about eDRAM (L4) cache at all.

We do hope to see more GPGPU-friendly features in upcoming versions now that Intel is taking graphics seriously. Perhaps with the forthcoming DG1 discrete graphics

GPGPU (Xe-LP G7) Performance Benchmarking

In this article we test GPGPU core performance; please see our other articles on:

• CPU
  • Intel Core Gen11 TigerLake ULV (i7-1165G7) Review & Benchmarks – CPU AVX512 Performance
  • Benchmarks of JCC Erratum Mitigation – Intel CPUs
  • Intel Core Gen10 IceLake ULV (i7-1065G7) Review & Benchmarks – CPU AVX512 Performance
  • AVX512 Improvement for Icelake Mobile (i7-1065G7 ULV)
• GPGPU
  • Intel DG1 (Iris Xe Max Gen12) Review & Benchmarks – GPGPU Performance
  • Intel Iris Plus G7 Gen11 IceLake ULV (i7-1065G7) Review & Benchmarks – GPGPU Performance

Hardware Specifications

We are comparing the middle-range Intel integrated GP-GPUs with previous generation, as well as competing architectures with a view to upgrading to a brand-new, high performance, design.

Disclaimer

This is an independent article that has not been endorsed or sponsored by any entity (e.g. Intel). All trademarks acknowledged and used for identification only under fair use. Errors and omissions excepted (E&OE).

The article contains only public information available elsewhere on the Internet and not provided under NDA or embargoed. At publication time, the products have not been directly tested by SiSoftware and thus the accuracy of the benchmark scores cannot be verified; however, they appear consistent and do not appear to be false/fake.

Processing Performance

We are testing both OpenCL performance using the latest SDK / libraries / drivers from both Intel and competition.

Results Interpretation: Higher values (GOPS, MB/s, etc.) mean better performance.

Environment: Windows 10 x64, latest Intel and AMD drivers. Turbo / Boost was enabled on all configurations.

Processing Benchmarks		Intel Iris XE-LP G7 96EV	Intel XE-LP G1 32EV	Intel Iris Plus (IceLake) G7 64EV	AMD Vega 8 (Ryzen5) 8CU	Comments
	Mandel FP16/Half (Mpix/s)	4,342 [+54%]	1,419	2,820	2,000	Xe beats EV11 by over 50% using FP16!
	Mandel FP32/Single (Mpix/s)	2,062 [+55%]	654	1,330	1,350	Standard FP32 is just as fast, 55% faster.
	Mandel FP64/Double (Mpix/s)	98.6* [+41%]	31.3*	70*	111	Without native FP64 support Xe craters like old EV11.
	Mandel FP128/Quad (Mpix/s)	9.91* [+31%]	3.49*	7.54*	7.11	Emulated FP128 is even harder for Xe.
Starting off, we see almost perfect scaling with improvement in EUs, with Xe 50% faster than old EV11. Unfortunately, again without native FP64 support – it cannot match the competition. For FP64 workloads – you’ll have to use the CPU; for ULV that may be OK but for discrete DG1 that is not so great. * Emulated FP64 through FP32.
	Crypto AES-256 (GB/s)	7.9 [+3x]	2.54	2.6	2.58	Integer performance is 3x faster than EV11
	Crypto AES-128 (GB/s)		3.54	3.38	3.3	Nothing much changes when changing to 128bit.
	Crypto SHA2-256 (GB/s)	20.52 [+3x]	6.81	6.9	14.29	Xe beats Vega even with its acceleration.
	Crypto SHA1 (GB/s)		13.34	14.18	18.77	With 128-bit Xe is even faster.
	Crypto SHA2-512 (GB/s)			2.26	3.36	64-bit integer workload is also stellar.
Despite our sample using slower DDR4 memory vs. LP-DDR4x ICL/EV11, integer performance is 3x faster – a huge upgrade. It even manages to beat AMD’s Vega with its crypto acceleration instructions (media ops). While the crypto currency frenzy has died out (not likely to mine coins on ULV GP-GPUs), the dedicated DG1 may be a serious crypto-craker GPU.
	Black-Scholes float/FP16 (MOPT/s)		1,111	2,340	1,720	With FP16 we see G7 win again by ~35%.
	Black-Scholes float/FP32 (MOPT/s)	1,603 [+22%]	993	1,310	829	With FP32 Xe is 22% faster.
	Binomial half/FP16 (kOPT/s)		116	292	270	Binomial uses thread shared data thus stresses the memory system.
	Binomial float/FP32 (kOPT/s)	334 [+14%]	111	292	254	With FP32, XE is just 15% faster.
	Monte-Carlo half/FP16 (kOPT/s)		470	667	584	Monte-Carlo also uses thread shared data but read-only.
	Monte-Carlo float/FP32 (kOPT/s)	1,385 [+94%]	444	719	362	With FP32 code Xe is 2x faster than EV11.
For financial FP32/FP16 workloads, Xe is not always much faster than EV11, with two algorithms just 15-22% faster but one 2x as fast. Again, due to lack of FP64 support – it cannot run high-precision workloads which may be a problem for some algorithms. This does not bode well for the dedicated DG1 as it would be the only discrete card without native FP64 support unlike competition. However, it is likely (some) FP64 units will be included unless Intel will aim it squarely to gamers (only).
	HGEMM (GFLOPS) float/FP16		528	563	884	Vega still has great performance with FP16.
	SGEMM (GFLOPS) float/FP32	683 [+64%]		419	314	With FP32, Xe is 64% faster than EV11.
	HFFT (GFLOPS) float/FP16		33.32	61.4	61.34	Vega does very well here also with FP16.
	SFFT (GFLOPS) float/FP32	52.7 [+34%]		39.2	31.5	With FP32, Xe is 34% faster.
	HNBODY (GFLOPS) float/FP16		652	930	623	All Intel GPUs do well here.
	SNBODY (GFLOPS) float/FP32	908 [+60%]		566	537	With FP32, Xe is 60% faster.
On scientific algorithms, Xe does much better and manages 35-65% better performance than EV11 and generally trouncing Vega on FP32 though not quite on FP16. Shall we mention lack of FP64 again?
	Blur (3×3) Filter single/FP16 (MPix/s)			3,520	2,273
	Blur (3×3) Filter single/FP32 (MPix/s)	4,725 [+3x]	1,649	1.570	782	In this 3×3 convolution algorithm, Xe is 3x faster!
	Sharpen (5×5) Filter single/FP16 (MPix/s)			1,000	582
	Sharpen (5×5) Filter single/FP32 (MPix/s)	1,354 [+4.2x]	436	319	157	Same algorithm but more shared data, Xe is 4x faster.
	Motion Blur (7×7) Filter single/FP16 (MPix/s)			924	619
	Motion Blur (7×7) Filter single/FP32 (MPix/s)	727 [+2.2x]	232	328	161	With even more data Xe is 2x faster.
	Edge Detection (2*5×5) Sobel Filter single/FP16 (MPix/s)			1,000	595
	Edge Detection (2*5×5) Sobel Filter single/FP32 (MPix/s)	1,354 [+4.26x]	435	318	155	Still convolution but with 2 filters – 4.3x faster.
	Noise Removal (5×5) Median Filter single/FP16 (MPix/s)			26.63	7.69
	Noise Removal (5×5) Median Filter single/FP32 (MPix/s)	35.73 [+33%]	16.27	26.91	4.06	Different algorithm Xe just 33% faster.
	Oil Painting Quantise Filter single/FP16 (MPix/s)			24.34
	Oil Painting Quantise Filter single/FP32 (MPix/s)	23.95 [+22%]	11.11	19.63	2.59	Without major processing, Xe is only 22% faster.
	Diffusion Randomise (XorShift) Filter single/FP16 (MPix/s)			1,740	2,091
	Diffusion Randomise (XorShift) Filter single/FP32 (MPix/s)	2,772 [+48%]	1,175	1,870	2,100	This algorithm is 64-bit integer heavy thus G7 is 10% slower
	Marbling Perlin Noise 2D Filter single/FP16 (MPix/s)			215	1,046
	Marbling Perlin Noise 2D Filter single/FP32 (MPix/s)	916 [-4%]	551	950	608	One of the most complex and largest filters, Xe ties with EV11.
For image processing tasks, Xe seems to do best, with up to 4x better performance – likely due to updated compiler and drivers. In any case for such tasks, upgrading to TGL will give you a huge boost. (fortunately no FP64 processing here)

Memory Performance

We are testing both OpenCL performance using the latest SDK / libraries / drivers from Intel and competition.

Results Interpretation: For bandwidth tests (MB/s, etc.) high values mean better performance, for latency tests (ns, etc.) low values mean better performance.

Environment: Windows 10 x64, latest Intel and AMD drivers. Turbo / Boost was enabled on all configurations.

Memory Benchmarks		Intel Iris XE-LP G7 96EV	Intel XE-LP G1 32EV	Intel Iris Plus (IceLake) G7 64EV	AMD Vega 8 (Ryzen5) 8CU	Comments
	Internal Memory Bandwidth (GB/s)	44.92 [+27%]	45.9	36.3	27.2	Xe manages to squeeze more bandwidth of DDR4.
	Upload Bandwidth (GB/s)	7.75 [-54%]	7.7	17	4.74	Uploads are 1/2 slower at this time.
	Download Bandwidth (GB/s)	7.6 [-58%]	7.6	185		Download bandwidth is not much better.
Thanks to the faster LP-DDR4X memory, Xe has even higher bandwidth than EV11; with future DDR5 / LPDDR5 this will increase even higher. At this time, perhaps due to the driver the upload/download bandwidths are 1/2x lower.

SiSoftware Official Ranker Scores

• • 11th Gen Intel Core i7-1165G7

Final Thoughts / Conclusions

Once again Intel seems to be taking graphics seriously: for the 2nd time in a row we have a major graphics upgrade with Xe with big upgrades in EV cores (count), performance and bandwidth. Overall it seems to be 50% faster than EV11 with lower-end devices benefiting most from the upgrade. While the competition was unassailable – Intel has managed to close the gap and overtake.

However, this is still a core aimed at gamers and it does not provide much for GP-GPU; the improved integer performance is very much welcome – 3-times better (!) but few and specific instructions for AI only. Lack of FP64 makes it unsuitable for high-precision financial and scientific workloads; something that the old EV7-9 cores could do reasonably well (all things considered).

For integrated graphics, this is not a problem – not many people would expect ULV GPU core to run compute-heavy workloads; however, the dedicated DG1 card would really be out-spec’d by the competition, with even old, low-end devices providing more features. However, dedicated DG1 is likely to include (some) FP64 units and/or additional units unlike the low-power (LP ULV) integrated versions.

Getting back to ULV, Xe-LP’s performance completely obsoletes devices (e.g. SKL/KBL/WHL/CML-ULV) using the older EV9x cores – unless you really don’t plan on using them except for “business 2D graphics” or displaying the desktop.

If you have not upgraded to ICL yet, TGL is a far better, compelling, proposition that should be your (current) top choice for long-term use. For ICL owners, there is still a lot to upgrade though not as massive as anything released previously.

In a word: Highly Recommended – 8/10!

Please see our other articles on:

• Intel Iris Plus G7 Gen11 IceLake ULV (i7-1065G7) Review & Benchmarks – GPGPU Performance
• Intel Core Gen10 IceLake ULV (i7-1065G7) Review & Benchmarks – CPU AVX512 Performance

Disclaimer

This is an independent article that has not been endorsed or sponsored by any entity (e.g. Intel). All trademarks acknowledged and used for identification only under fair use. Errors and omissions excepted (E&OE).

The article contains only public information available elsewhere on the Internet and not provided under NDA or embargoed. At publication time, the products have not been directly tested by SiSoftware and thus the accuracy of the benchmark scores cannot be verified; however, they appear consistent and do not appear to be false/fake.