nVidia Titan X: Pascal GPGPU Performance in CUDA and OpenCL

What is “Titan X (Pascal)”?

It is the current high-end “pro-sumer” card from nVidia using the current generation “Pascal” architecture – equivalent to the Series 10 cards. It is based on the 2nd-from-the-top 102 chipset (not the top-end 100) thus it does not feature full speed FP64/FP16 performance that is generally reserved for the “Quadro/Tesla” professional range of cards. It does however come with more memory to fit more datasets and is engineered for 24/7 performance.

Pricing has increased a bit from previous generation X/XP but that is a general trend today from all manufacturers; we will see if the performance justifies this (price efficiency) and also whether the power increase is also worth it (price efficiency).

See these other articles on Titan (and competition) performance:

• AMD Radeon RX 6000 (RDNA2, Navi2) Review & Benchmarks – GPGPU Performance
• nVidia RTX 3090, 3080: Ampere GPGPU performance in CUDA and OpenCL
• nVidia Titan RTX / 2080Ti: Turing GPGPU performance in CUDA and OpenCL
• AMD Radeon 5700XT: Navi GPGPU Performance in OpenCL
• nVidia Titan V : Volta GPGPU performance in CUDA and OpenCL

Hardware Specifications

We are comparing the top-of-the-range Titan X with previous generation cards and competing architectures with a view to upgrading to a mid-range high performance design.

nVidia Titan X (Pascal)

Processing Performance

We are testing both CUDA native as well as OpenCL performance using the latest SDK / libraries / drivers from both nVidia and competition.

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

Environment: Windows 10 x64, latest nVidia drivers 398.36, CUDA 9.2, OpenCL 1.2. Turbo / Boost was enabled on all configurations.

Processing Benchmarks		nVidia Titan X CUDA/OpenCL	nVidia GTX 980 CUDA/OpenCL	AMD Vega 56 OpenCL	AMD Fury 64 OpenCL	Comments
	Mandel FP32/Single (Mpix/s)	17,870 [37%] / 16,000	7,000 / 6,100	13,000	8,720	Titan X makes a good start beating the Vega by almost 40%.
	Mandel FP16/Half (Mpix/s)	245 [-98%] / n/a	n/a	13,130	7,890	FP16 is so slow that it is unusable – just for testing.
	Mandel FP64/Double (Mpix/s)	661 [-47%] / 672	259 / 265	1,250	901	FP64 is also quite slow though a lot faster than on the GTX 980.
	Mandel FP128/Quad (Mpix/s)	25 [-67%] / 24	10.8 / 10.7	77.3	55	Emulated FP128 precision depends entirely on FP64 performance and thus is… slow.
With FP32 “normal” workloads Titan X is quite fast, ~40% faster than Vega and about 2.5x faster than an older GTX 980 thus quite an improvement. But FP16 workloads should not apply – better off with FP32 – and FP64 is also about 1/2 the performance of a Vega – also slower than even a Fiji. As long as all workloads are FP32 there should be no problems.
	Crypto AES-256 (GB/s)	40 [-38%] / 38	16 / 16	65	46	Titan X is a lot faster than previous gen but still ~40% slower than a Vega
	Crypto AES-128 (GB/s)	52 [-38%] / 51	23 / 21	84	60	Nothing changes here , the X still about 40% slower than a Vega.
	Crypto SHA2-256 (GB/s)	134 [+4%] / 142	58 / 59	129	82	In this integer workload, somehow Titan X manages to beat the Vega by 4%!
	Crypto SHA1 (GB/s)	107 [-34%] / 114	50 / 54	163	124	SHA1 is mysteriously slower thus the X is ~35% slower than a Vega.
	Crypto SHA2-512 (GB/s)	72 [+2.3x] / 42	32 / 24	31	13.8	With 64-bit integer workload, Titan X is a massive 2.3x times faster than a Vega.
Historically, nVidia cards have not been tuned for integer workloads, but Titan X still manages to beat a Vega – the “gold standard” for crypto-currency hashing – on both SHA256 and especially on 64-bit integer SHA2-512! Perhaps for the first time a nVidia card is competitive on integer workloads and even much faster on 64-bit integer workloads.
	Black-Scholes float/FP32 (MOPT/s)	11,480 [+28%] / 11,470	5,280 / 5,280	9,000	11,220	In this FP32 financial workload Titan X is almost 30% faster than a Vega.
	Black-Scholes double/FP64 (MOPT/s)	1,370 [-36%] / 1,300	547 / 511	1,850	1,290	Switching to FP64 code, the X remains competitive and is about 35% slower.
	Binomial float/FP32 (kOPT/s)	2,240 [-8%] / 2,240	1,200 / 1,140	2,440	1,760	Binomial uses thread shared data thus stresses the SMX’s memory system and here Vega surprisingly does better by 8%
	Binomial double/FP64 (kOPT/s)	129 [-20%] / 133	51 / 51	161	115	With FP64 code the X is only 20% slower than a Vega.
	Monte-Carlo float/FP32 (kOPT/s)	5,350 [+47%] / 5,150	2,140 / 2,000	3,630	2,470	Monte-Carlo also uses thread shared data but read-only thus reducing modify pressure – here Titan X is almost 50% faster!
	Monte-Carlo double/FP64 (kOPT/s)	294 [-34%] / 267	118 / 106	385	332	Switching to FP64 the X is again 34% slower than a Vega.
For financial FP32 workloads, the Titan X generally beats the Vega by a good amount or at least ties with it; with FP64 precision it is about 1/2 the speed which is to be expected. As long as you have FP32 workloads this should not be a problem.
	SGEMM (GFLOPS) float/FP32	6,280 [+19%] / 6,600	2,550 / 2,550	5,260	3,630	Using 32-bit precision Titan X beats the Vega by 20%.
	DGEMM (GFLOPS) double/FP64	335 [-40%] / 332	130 / 129	555	381	With FP64 precision, unsurprisingly the X is 40% slower.
	SFFT (GFLOPS) float/FP32	242 [-20%] / 227	148 / 136	306	348	FFT does better with HBM memory and here Titan X is 20% slower than a Vega.
	DFFT (GFLOPS) double/FP64	207 / 191	89 / 82	139	116	Surprisingly the X does very well here and manages to beat all cards by almost 50%!
	SNBODY (GFLOPS) float/FP32	5,600 [+20%] / 4,870	2,100 / 2,000	4,670	3,080	Titan X does well in this algorithm, beating the Vega by 20%.
	DNBODY (GFLOPS) double/FP64	275 [-20%] / 275	82 / 81	343	303	With FP64 precision, the X is again 20% slower.
The scientific scores are similar to the financial ones but the gain/loss is about 20% not 40% – in FP32 workloads Titan X is 20% faster while in FP64 it is about 20% slower than a Vega – a closer result than expected.
	Blur (3×3) Filter single/FP32 (MPix/s)	14,550 [-60%] / 10,880	7,310 / 5,530	36,000	28,000	In this 3×3 convolution algorithm, somehow Titan X is over 50% slower than a Vega and even a Fury.
	Sharpen (5×5) Filter single/FP32 (MPix/s)	3,840 [-11%] / 2,750	1,870 / 1,380	4,300	3,150	Same algorithm but more shared data reduces the gap to 10% but still a loss.
	Motion Blur (7×7) Filter single/FP32 (MPix/s)	3,920 [-10%] / 2,930	1,910 / 1,440	4,350	3,200	With even more data the gap remains at 10%.
	Edge Detection (2*5×5) Sobel Filter single/FP32 (MPix/s)	3,740 [-11%] / 2,760	1,860 / 1,370	4,210	3,130	Still convolution but with 2 filters – Titan X is 10% slower again.
	Noise Removal (5×5) Median Filter single/FP32 (MPix/s)	35.7 / 55 [+52%]	20.6 / 25.4	36.3	30.8	Different algorithm allows the X to finally beat the Vega by 50%.
	Oil Painting Quantise Filter single/FP32 (MPix/s)	15.6 [-60%] / 15.3	12.2 / 11.4	38.7	14.3	Without major processing, this filter does not like the X much it runs 1/2 slower than the Vega.
	Diffusion Randomise (XorShift) Filter single/FP32 (MPix/s)	16,480 [-57%] / 14,000	7,600 / 6,640	38,730	28,500	This algorithm is 64-bit integer heavy but again Titan X is 1/2 the speed of Vega.
	Marbling Perlin Noise 2D Filter single/FP32 (MPix/s)	290 / 6,350 [+13%]	210 / 3,080	5,600	4,410	One of the most complex and largest filters, Titan X finally beats the Vega by over 10%.
For image processing using FP32 precision Titan X surprisingly does not do as well as expected – either in CUDA or OpenCL – with the Vega beating it by a good margin on most filters – a pretty surprising result. Perhaps more optimisations are needed on nVidia hardware. We obviously did not test FP16 performance at all as that would have been far slower.

Memory Performance

We are testing both CUDA native as well as OpenCL performance using the latest SDK / libraries / drivers from nVidia 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 nVidia drivers 398.36, CUDA 9.2, OpenCL 1.2. Turbo / Boost was enabled on all configurations.

Memory Benchmarks		nVidia Titan X CUDA/OpenCL	nVidia GTX 980 CUDA/OpenCL	AMD Vega 56 OpenCL	AMD Fury 64 OpenCL	Comments
	Internal Memory Bandwidth (GB/s)	356 [+13%] / 354	145 / 144	316	387	Titan X brings more bandwidth than a Vega (+13%) but the old Fury takes the crown.
	Upload Bandwidth (GB/s)	11.4 / 9	12.1 / 12	12.1	11	All cards use PCIe3 x16 and thus no appreciable delta.
	Download Bandwidth (GB/s)	12.2 / 8.9	11.5 / 12.2	10	9.8	Again no significant difference but we were not expecting any.
Titan X uses current GDDR5X but with high data rate allowing it to bring more bandwidth that some HBM2 designs – a pretty impressive feat. Naturally high-end cards using HBM2 should have even higher bandwidth.
	Global (In-Page Random Access) Latency (ns)	201 / 230	230	273	343	Compared to previous generation, Titan X has better latency due to higher data rate.
	Global (Full Range Random Access) Latency (ns)	286 / 311	306	399	525	Similarly, even full random accesses are faster,
	Global (Sequential Access) Latency (ns)	89 / 121	97	129	216	Sequential access has similarly low latencies but nothing special.
	Constant Memory (In-Page Random Access) Latency (ns)	117 / 174	126	269	353	Constant memory latencies have also dropped.
	Shared Memory (In-Page Random Access) Latency (ns)	18 / 53	21	49	112	Even shared memory latencies have dropped likely due to higher core clocks.
	Texture (In-Page Random Access) Latency (ns)	195 / 196	208		121	Texture access latencies have come down as well.
	Texture (Full Range Random Access) Latency (ns)	282 / 278	308			And even full range latencies have decreased.
	Texture (Sequential Access) Latency (ns)	87 /123	102			With sequential access there is no appreciable delta in latencies.
We’re only comparing CUDA latencies here (as OpenCL is quite variable) – thus compared to the previous generation (GTX 980) all latencies are down, either due to higher memory data rate or core clock increases – but nothing spectacular. Still good progress and everything helps.
We see L1 cache effects until 16kB (same as previous arch) and between 2-4MB tallying with the 3MB cache. While fast perhaps they could be a bit bigger.
As with global memory we see the same L1D and L2 cache affects with similar latencies. All in all good performance but we could do with bigger caches.

Titan X’s memory performance is what you’d expect from higher clocked GDDR5X memory – it is competitive even with the latest HBM2 powered competition – both bandwidth and latency wise. There are no major surprises here and everything works nicely.

SiSoftware Official Ranker Scores

• nVidia Tesla P100-PCIE-16GB (Pascal)
• nVidia Titan X (Pascal)

Final Thoughts / Conclusions

Executive Summary: Great update, big performance increase: Gold Star 9/10!

Titan X based on the current “Pascal” architecture performs very well in FP32 workloads – it is much faster than previous generation for a modest price increase and is competitive with the AMD’s Vega offers. But it is likely due to be replaced soon as next-generation “Volta” architecture is already out on the high-end (Titan V) and likely due to filter down the stack to both consumer (Series 11?) cards and “pro-sumer” cheaper Titan cards than the Titan V.

For FP64 workloads it is perhaps best to choose an older Quadro/Tesla card with more FP64 units as performance is naturally much lower. FP16 performance is also restricted and pretty much not usable – good for compatibility testing should you hope to upgrade to a full-speed FP16 card in the future. For both these workloads – the high-end Titan V is the card you probably want – but at a much higher price.

Still for the money, Titan X has its place and the most common FP32 workloads (financial, scientific, high precision image processing, etc.) that do not require FP64 nor FP16 optimisations perform very well and this card is all you need.

nVidia Titan X (Pascal)