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Benchmarking Intel vs. ARM (Graviton) for Splitting, Compressing, Hashing, and Encrypting Data

· 9 min read
Niraj Tolia

Heat map with data

This article benchmarks the performance of the compute-intensive components found in modern backup applications on Intel and ARM (Graviton) architectures. The results around performance and cost-efficiency are illuminating as we expected most of third-party hashing and encryption libraries to be primarily optimized for Intel and not ARM.

Why we care

To improve security and efficiency (storage used, network transfers) as well as to reduce cost, Corso, our free open-source backup tool for Microsoft 365, uses encryption and deduplication under the hood. From a security perspective, this ensures that no third party, including your cloud provider, can ever read your data. From an efficiency perspective, no duplicate copies of your data are stored or even transferred to the cloud provider. Therefore, if you accidentally have duplicated files in OneDrive or the same file or email shows up in multiple backups, only one copy of the data will be stored in the cloud and will only be transferred once.

However, given the large amounts of data that Corso might transfer, we wanted to look at both performance and cost-efficiency of running Corso on ARM (Graviton) vs. Intel and the results are quite striking!

The takeaways from the benchmarking include:

  • Overall, ARM outperforms Intel for data-intensive tasks required by backup applications and the difference can be as high as 350%.

  • ARM is a much better fit for data-intensive processing that can be parallelized than for single-thread workloads

  • The lower cost of ARM further amplify its advantages if you are focused of delivering more throughput for every dollar spent

  • While ARM might not be ideal for some parts of the data-intensive deduplication pipeline, the overall win is higher

While our benchmarking is extremely suggestive about the efficiency of ARM for these specific tasks, if compute time is a significant part of your spend we encourage you to do your own benchmarking. If you get different results we’d love to hear about it! Join our Discord to continue the conversation

Experimental setup

To ensure that our results would be as comparable as possible, we picked an Intel and ARM VM running in AWS in us-east-1. These are the configurations of the systems:

ArchitectureAWS Instance TypevCPUsMemory (GiB)Cost ($/hour)


In Corso, we use Kopia as our data processing engine. It first split the data to divide larger files into smaller chunks for efficient deduplication. That split data is then deduplicated through the use of cryptographically strong hashing algorithms, compressed, and then encrypted.

Based on this data pipeline, we split our measurements into 5 different parts to microbenchmark the different parts of the above process. For each of the below microbenchmarks, we also varied internal benchmark parallelism between 1, 4, and 8 parallel goroutines.

  • Splitting: Buzhash vs. Rabin-Karp algorithms
  • Compression: Different variants of deflate, S2, lz4, zstd, gzip, and pgzip
  • Hashing: Different BLAKE and HMAC-SHA variants
  • Encryption: AES256-GCM and ChaCha20-Poly1305
  • Hashing + Encryption: Combination of the above Encryption and Hashing algorithms

ARM vs. Intel performance results

Given the large number of results collected (there are 372 distinct performance data points!), we will only be presenting a subset of the results collected for the splitting, compression, and combined hashing and encryption microbenchmarks.

Within these results, we will focus on the numbers with parallelism set to 8 and will focus on throughput numbers (higher is better) measured in MiB/s. For easier visualization, we also present the relative performance where we compare the throughput of ARM vs. Intel. For a more price-accurate comparison, we also present the price-adjusted relative performance where we see how many more MiB/s we can process for the same dollar.

While the results shown here are truncated for brevity, feel free to give us a shout on Discord if you want to see the raw data.


Let’s first look at splitting, a critical part of data deduplication, that uses rolling-hash functions. While there is a lot more information available elsewhere, a rolling hash is a type of hash function that operates on a sliding window of data, allowing it to efficiently compute the hash value for a given chunk of data without having to process the entire dataset. This is useful in deduplication because it allows Corso and Kopia to quickly compare the hash values of different chunks of identical data, even if they're not the same size or don't start at the same point in the dataset.

The size of the blocks and the algorithm used to divide the dataset are important considerations in deduplication, as they can affect the speed and efficiency of deduplication. For these results we compare the performance of Buzhash vs. the Rabin-Karp algorithm with block sizes of 1MiB, 2MiB. 4MiB, and 8MiB.

Throughput of Corso splitters

Relative performance of Corso splitters

As we can see in the results, Buzhash is faster than Rabin-Karp for all block sizes. Further, for Buzhash, ARM is 150% faster than Intel and 190% on a per-dollar price-adjusted basis.

While not presented here, it should be noted that with parallelism of 1, splitting on ARM was only 60% and 80% of Intel’s performance for Buzhash and Rabin-Karp respectively. This does show that ARM might be a bad choice for single-threaded applications.


For backups, we're limited to using lossless compression algorithms where there is no room for data degradation. The compression algorithm selection is driven by various tradeoffs such as their effectiveness at reducing data size, computational efficiency, and the speed. Some algorithms may be effective at reducing data size but may require a lot of computational resources while others may be less effective at reducing the size of a file but may be much faster to execute. For this experiment, the compression algorithms were run on an large file (100MiB) that was generated from random data and was therefore not compressible.

Throughput of Corso compression algorithms

Relative performance of Corso compression algorithms

When we look at absolute and relative performance results above, we see that the absolute performance of various compression algorithms on ARM is almost equivalent (90%+) of Intel in the worst case (gzip) and up to 300% better (s2-parallel-4). The difference is even starker on a price-adjusted basis where ARM is delivering a 380% improvement over Intel on a per-dollar basis.

While not presented here, it should again be noted that with parallelism of 1, compression on ARM was slower for some compression algorithms (gzip, zstd) and some variants (deflate-best-compression, pgzip-best-compression). This once again highlights the power of ARM when used in multi-thread applications and the need to benchmark for your workload.

Hashing + encryption

Two critical components are left to complete the deduplication data path in Corso and Kopia. A unique key (hash) for the data needs to be generated for comparing identical data chunks and this is accomplished via either the BLAKE hashing algorithm or a SHA hashing algorithm with an HMAC. HMAC, or Keyed-Hash Message Authentication Code, uses a cryptographic hash function in combination with a secret key. This key is used to authenticate the data being hashed and can be used later to ensure that it hasn't been tampered with. The data is then also encrypted using an encryption algorithm (for example, AES256-GCM or ChaCha20-Poly1305).

For this experiment, we benchmarked the combination of different hashing and encryption algorithms but only present the SHA variant HMAC results here. All of the BLAKE variants on ARM significantly underperformed Intel (between 20--80% of Intel’s performance) and we believe that this is because the underlying library being used has no ARM-specific optimizations but it does have optimizations for Intel platforms with AVX2/SSE4.1.

For the below results, the first part of the chart label is the hashing algorithm used (HMAC is used with every SHA variant) while the second part is the encryption algorithm.

Throughput of Corso hashing and encryption algorithms

Relative performance of Corso hashing and encryption algorithms

As seen above, with a parallelism of 8, hashing and encryption on ARM never underperforms Intel on an absolute basis and always outperforms on a price-adjusted basis. In particular, ARM can significantly outperform Intel processors by 350% on an absolute basis and by up to 440% on a price-adjusted basis.

When we teased apart the hashing and encryption results, we noticed that hashing, for the HMAC-SHA-256 variants were up to 700% (absolute)/870% (price adjusted) faster on ARM. In contrast, AES-256encryption throughput on ARM is only 90% that of Intel’s at a parallelism of 8 but, price adjusted, it can process 120% more data for the same cost while ChaCha20-Poly1305's performance is between 40% (absolute) and 50% (price adjusted) of Intel’s. However, given that relatively more work performed in hashing, the difference disappears when the combined work is taken into account.

While not presented here, it should again be noted that with parallelism of 1, hashing and encryption on ARM was slower when using SHA3 HMAC variants for hashing but ARM starts to outperform with parallelism of 4 and above. This once again highlights the applicability of ARM to multi-threaded applications.


The above results show how exciting ARM is as an architecture for us data-processing nerds because of the performance and cost optimizations possible. The improvement is a result of the hardware acceleration available on the ARM platform used for benchmarking (AWS Graviton) and the additional optimizations in the libraries used in our software. While these things may constantly shift, we're excited about the advances and the benefits for users of backup software.

If you have questions, want access to raw data, or want to talk about about future benchmarking, join us on Discord or follow us on Twitter to get the conversation started. We look forward to hearing from you!