Performance Report

Comprehensive performance analysis of Phala Cloud’s TEE infrastructure.

TL;DR - Performance overhead compared to non-TEE environments:

• CPU: 5-10% slower in single-threaded, scales to 80% efficiency at 32 cores
• GPU: 6.9x faster than AWS GPUs for INT8 workloads. For LLM inference, TEE adds 5-20% overhead
• IO: Matches bare-metal for sequential I/O (36.2 GB/s), 40% higher latency on random reads
• Network: 5-7x higher latency for HTTPS, but gateway handles 1,244 QPS at 1,000 concurrent connections

I. Introduction

TEE Performance Overview

Phala Cloud’s TEE infrastructure delivers enterprise-grade performance with strong security guarantees. The following sections detail our comprehensive benchmarks across key performance dimensions.

• Compute Performance
  Phala’s TEE environment demonstrates exceptional computational efficiency:

  • CPU: 1,271 MIPS for per-core compression
  • Scaling: 79.91% scaling efficiency at 32 threads
  • Multi-core: < 10% slower than bare-metal at 8 threads
  • GPU: 3,341 TFLOPS for INT8 operations (6.9x AWS GPUs)

• Storage I/O
  Storage performance shows minimal overhead for most real-world scenarios:

  • Random Read: ~40% latency increase in TEE
  • Sequential I/O: Outperforms bare-metal at 36.2 GB/s
  • Caching: Significantly improves random read performance

• Network & Security
  The architecture maintains robust performance under demanding conditions:

  • Gateway: 1,244 QPS at 1,000 concurrent connections
  • HTTPS: 702% latency increase vs HTTP (mitigated by optimizations)

• Zero-Knowledge Proofs
  Our implementation balances security and performance:

  • Baseline: 5.12x slowdown (18.3 kHz vs non-TEE)
  • GPU Acceleration: Reduces overhead to < 1.8x
  • Throughput: 25.1k proofs/second (GPU-accelerated)

Performance Highlights

Component	Key Metric	Performance	Key Insight
GPU Performance	INT8 Compute	3,341 TFLOPS	6.9x faster than AWS GPUs
Single-thread CPU	Compression Speed	1,271 MIPS	5% - 10% slower than bare metal
Multi-core Scaling	32-core Efficiency	79.91%	Near-linear scaling with core count
Sequential I/O	Write Throughput	36.2 GB/s	Outperforms bare-metal performance
Network Throughput	QPS @1000cc	1,245	6x higher than standard web servers
Security Overhead	zkProof Verification	18.3 kHz	5.1x security overhead (vs non-TEE)

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II. Storage Performance Analysis [1]

Experiment Setup

# Run  
docker run -it --rm -v /fio:/data infrabuilder/fio

# https://github.com/InfraBuilder/docker-fio/blob/main/benchmark.sh

Performance Summary

Metric	H200 Host	H200 CVM	TDX-Lab Host	TDX-Lab CVM	TDX-Lab VM
Random R/W IOPS (k)	259/156	33/34	97.3/32	43/37	72/75
Random R/W Bandwidth (MiB/s)	3642/3356	2071/1389	519/381	1080/1193	12253/4215
Avg Latency R/W (μs)	85/24	892/955	213/99	482/190	63/68
Sequential R/W (MiB/s)	4338/3413	9973/2293	526/377	1168/412	36230/3829
Mixed R/W IOPS (k)	129/43	18.7/6.2	67.7/23.5	43.6/14.5	71/23

Key Findings

1. TEE CVM shows 60-80% I/O performance degradation vs bare-metal
2. Memory encryption extends I/O path and introduces context-switching overhead
3. Random operations most impacted (20% of bare-metal performance)
4. Sequential Read speed is faster in TEE VM due to that qemu may cache the memory

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III. Network Gateway Performance [2]

Experiment Setup

ab -n 5000 -c CONCURRENCY_NUMBER https://e7cc25b0992a0e16b3377652efca9c0a6559d407-8090.app.kvin.wang:12004/prpc/Version

Core Performance: Prod5 vs TDX-Lab

Metric	Concurrency	Prod5	TDX-Lab	Gain	Latency Advantage
QPS	200	206.46	1189.93	+476%	5.76x
	500	194.70	1228.82	+531%	6.31x
	1000	179.33	1244.85	+594%	6.94x
	2000	182.39	1038.60	+469%	5.70x
P99 Latency (ms)	200	1,254	173	-86%	7.25x
	500	4,099	455	-89%	9.01x
	1000	7,768	822	-89%	9.45x
	2000	19,950	1,672	-92%	11.93x
Error Rate	2000	1.36%	0%	-100%	Absolute advantage
Max Connect Time (ms)	2000	27,084	1,677	-94%	16.15x

TProxy Overhead Analysis

Access Method	Environment	QPS	Avg Latency	Overhead Source
Direct (HTTP)	TDX-Lab	11,132	89.8ms	Baseline
TProxy (HTTPS)	TDX-Lab	1,264	791.3ms	+702% latency

Multi-process AB Test

• Experiment Setup
• gateway ver: git 7bc9eea958bd8aaca228341139f2cff5fab1d8d9

URL=https://health.app.kvin.wang:18714/
AB="ab -n 5000 -c 40 $URL"
for _ in `seq 1 50`; do
$AB &
done
$AB
sleep 1

• Results

Environment	Gateway Ver.	Log Level	Total QPS	CPU Usage	Bottleneck
TDX-Lab	7bc9eea	error	15,000	94.5%	CPU saturation
Prod8	7bc9eea	error	22,400	19.2%	Network interrupt
Prod8	7bc9eea	info	15,000	14.7%	Log I/O blocking

Conclusions

1. TDX-Lab outperforms Prod5 across all concurrency levels
2. TLS handshake accounts for 70% of TProxy overhead
3. Info-level logging reduces Prod8 performance by 33%

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IV. zkTLS Performance in TEE [3]

Core Performance (2048-bit Verification)

Environment	Total Time (s)	Proof Time (s)	Speed (kHz)	Proof Size (bytes)	TEE Overhead
CPU	166.54	166.30	98,479	8,340,752	Baseline
TEE CPU	628.78	628.58	24,704	25,123,323	3.78x
TEE GPU	852.98	852.73	18,312	25,123,323	5.12x

Memory Encryption Impact

Environment	Hash Speed (MB/s)	Memory BW Utilization
CPU	44.50	100%
TEE CPU	11.77	26.4%
TEE GPU	8.68	19.5%

Key Findings

1. Memory encryption causes 70-80% bandwidth degradation
2. Data structure padding increases proof size by 289%
3. Data migration overhead increases by 200% in TEE GPU

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V. Multi-threaded Computing Capability [4]

Experiment Setup

7z b -mmt8

Compression Performance Benchmark

System Config	Compress MIPS	Cores	Per-core Efficiency
TDX-Lab	40,677	32	1,271
Prod8	31,658	288	110
Prod5	12,654	128	99

Conclusions

1. TDX-Lab excels in compute-intensive tasks (high single-core frequency)
2. Prod8 leads in memory-bound operations (DDR5 advantage)
3. Prod5 suffers from frequency instability (48.7% fluctuation)

VI. zkVM + TEE GPU Integration [5]

Hardware Comparison

Metric	H200 NVL	AWS G6 (L4)	Advantage
Memory Capacity	141GB	24GB	5.9x
Memory Bandwidth	4.8TB/s	300GB/s	16x
INT8 Compute	3341 TFLOPS	485 TFLOPS	6.9x
Hourly Cost	$2.5	$0.805	3.1x
Cost-Performance Ratio	1.0	0.45	2.2x

Cost-Performance Ratio = (Compute/Cost) / H200 Baseline

Integration Benefits

1. Seamless deployment: SP1 zkVM runs on TEE GPU without code modifications
2. Dual security: Hardware encryption + cryptographic verifiability
3. Memory advantage: Supports complex workloads (zkEVMs, 100B+ parameter models)
4. Optimization headroom: Current utilization < 30% of available resources

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VII. TEE Scalability in Large-Scale LLM Inference [6]

Performance Metrics Across Models

• Table 1: Throughput Comparison (Tokens/Requests per Second)*

GPU	Model	TPS (TEE-on)	TPS (TEE-off)	TPS Overhead	QPS (TEE-on)	QPS (TEE-off)	QPS Overhead
H100	Llama-3.1-8B	123.30	132.36	+6.85%	18.21	18.82	+3.22%
	Phi3-14B-128k	66.58	69.78	+4.58%	7.18	7.35	+2.31%
	Llama-3.1-70B	2.48	2.48	-0.13%	0.83	0.83	-0.36%
H200	Llama-3.1-8B	121.04	132.78	+8.84%	29.60	32.01	+7.55%
	Phi3-14B-128k	68.43	72.98	+6.24%	12.83	13.86	+7.41%
	Llama-3.1-70B	4.08	4.18	+2.29%	2.19	2.20	+0.63%

Table 2: Latency Metrics (Time in Seconds)

GPU	Model	TTFT (TEE-on)	TTFT (TEE-off)	TTFT Overhead	ITL (TEE-on)	ITL (TEE-off)	ITL Overhead
H100	Llama-3.1-8B	0.0288	0.0242	+19.03%	1.6743	1.5549	+7.67%
	Phi3-14B-128k	0.0546	0.0463	+18.02%	3.7676	3.5784	+5.29%
	Llama-3.1-70B	0.5108	0.5129	-0.41%	94.8714	95.2395	-0.39%
H200	Llama-3.1-8B	0.0364	0.0301	+20.95%	1.7158	1.5552	+10.33%
	Phi3-14B-128k	0.0524	0.0417	+25.60%	3.6975	3.4599	+6.87%
	Llama-3.1-70B	0.4362	0.4204	+3.75%	57.3855	55.9771	+2.52%

Key Findings

1. Inverse Efficiency Scaling

   • Overhead decreases exponentially with model size
   • 70B models show near-zero overhead (H100: -0.13% TPS, -0.41% TTFT)
   • 8B models sustain 6-25% overhead due to shorter compute phases

2. Computation/IO Asymmetry

   Model	GPU Time Dominance	TEE Overhead
   Llama-3.1-70B	99.2%	< 0.5%
   Phi3-14B-128k	95.7%	2-7%
   Llama-3.1-8B	88.3%	7-26%

3. Token Volume Law

   • Every 10k token increase reduces overhead by 37%
   • At >50k tokens, TEE efficiency exceeds 95%
   • Phi3-128k demonstrates 5.29% ITL overhead vs 10.33% for 8B model

VIII. CPU Scaling Efficiency Analysis [7]

Experiment Setup

for threads in 1 2 4 8 16 32; do echo -e "\n=== Running with $threads threads ===\n"; 7z b -mmt$threads; done

Comparative Multi-threading Performance

• Table 1: TEE CVM Scaling Performance (7-Zip Benchmark)*

Threads	CPU Usage (%)	Compression (KiB/s)	Decompression (KiB/s)	Total Rating	Scaling Efficiency
1	100	2,511	34,387	2,803	100.00%
2	200	9,450	68,316	7,889	140.75%
4	383	14,048	135,014	13,100	116.90%
8	760	27,361	268,126	25,821	115.17%
16	1,432	44,535	443,829	42,322	94.38%
32	2,811	72,284	783,537	71,665	79.91%

• Table 2: Bare Metal Scaling Performance (7-Zip Benchmark)*

Threads	CPU Usage (%)	Compression (KiB/s)	Decompression (KiB/s)	Total Rating	Scaling Efficiency
1	100	4,992	43,200	4,544	100% (baseline)
2	183	9,791	63,936	7,935	87.3%
4	382	18,895	109,687	14,689	80.9%
8	787	34,001	217,465	27,279	75.1%
16	1,559	71,892	437,145	56,853	78.1%
32	2,662	121,055	797,133	97,853	67.4%

Key Findings

1. Superlinear Scaling in TEE

   • At 2 threads: 140.75% efficiency vs bare metal’s 87.3%
   • At 4 threads: 116.9% efficiency (+36% advantage over bare metal)

2. Memory Encryption Optimization

   Thread Range	Efficiency Delta	Primary Benefit
   1-8 threads	+35.2% avg	Cache locality optimization
   16-32 threads	+12.1% avg	Reduced context-switching

3. Total Performance Impact

   • Single-thread penalty: 45% performance loss in TEE (2,803 vs 4,544)
   • 32-thread recovery: 73.2% of bare metal throughput (71,665 vs 97,853)

Scaling Characteristics

Metric	TEE Advantage	Technical Cause
Initial Scaling	+52.45% (2-thread)	Memory-bound workload optimization
Mid-range	+41.8% (4-thread)	Reduced hypervisor interference
High-core	+12.5% (32-thread)	NUMA-aware scheduling

Conclusions

1. TEE demonstrates superior scaling efficiency (avg +35% at ≤8 threads) due to encrypted memory access optimizations
2. Scaling beyond 16 threads becomes memory-bound, reducing TEE’s relative advantage
3. Maximum throughput reaches 73% of bare metal in fully-loaded scenarios

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References

1. IO Benchmark with FIO
2. Gateway Benchmark Analysis
3. zkTLS in TEE zkVM Benchmark
4. TDX Host Benchmark
5. SP1 zkVM in TEE H200 Performance Benchmark
6. TEE Scalability in Large-Scale LLM Inference. (2024). arXiv:2409.03992
7. dstack CPU Benchmark

Note: Internal reports are available upon request. Please contact the Phala Team for access to the full documentation.

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