Introduce GPU feature in README (alibaba#112)

* add badge link to Graphscope
* Introduce GPU feature in README
* Update the download link of datasets in Performance.md

Signed-off-by: septicmk <[email protected]>

Author: mengke-mk

Performance.md

@@ -1,6 +1,6 @@
 # Performance
 
-We evaluated performance of **libgrape-lite** with [LDBC Graph Analytics Benchmark](http://graphalytics.org/). In addition to the ease of programming, we find that **libgrape-lite** achieves high performance comparably to the state-of-the-art systems. The experiments were conducted on 4 instances of [r6.8xlarge](https://www.alibabacloud.com/help/doc-detail/25378.htm#d12e563) on [AlibabaCloud ECS](https://www.alibabacloud.com/product/ecs), each with 32 threads, over LDBC XL-size datasets. Instances are imaged with [Aliyun Linux (a CentOS-variant)](https://www.alibabacloud.com/help/doc-detail/111881.htm).
+We evaluated performance of **libgrape-lite** with [LDBC Graph Analytics Benchmark](http://graphalytics.org/). In addition to the ease of programming, we find that **libgrape-lite** achieves high performance comparably to the state-of-the-art systems. The experiments were conducted on 4 instances of [r6.8xlarge](https://www.alibabacloud.com/help/doc-detail/25378.htm#d12e563) on [AlibabaCloud ECS](https://www.alibabacloud.com/product/ecs), each with 32 threads, over LDBC XL-size datasets. Instances are imaged with [Aliyun Linux (a CentOS-variant)](https://www.alibabacloud.com/help/doc-detail/111881.htm). The datasets can be downloaded from [here](https://ldbcouncil.org/benchmarks/graphalytics/).
 
 We compared **libgrape-lite** with [PowerGraph](https://github.com/jegonzal/PowerGraph)(commit a038f97
 ) [GeminiGraph](https://github.com/thu-pacman/GeminiGraph)(commit 170e7d3

README.md

@@ -9,6 +9,7 @@
 
 [![C/C++ CI](https://github.com/alibaba/libgrape-lite/workflows/C++%20CI/badge.svg)](https://github.com/alibaba/libgrape-lite/actions?workflow=C++%20CI)
 [![codecov](https://codecov.io/gh/alibaba/libgrape-lite/branch/master/graph/badge.svg)](https://codecov.io/gh/alibaba/libgrape-lite)
+[![GraphScope](https://img.shields.io/badge/Enables-Graphscope-blue)](https://github.com/alibaba/GraphScope)
 
 **libgrape-lite** is a C++ library from Alibaba for parallel graph processing. It differs from prior systems in its ability to parallelize sequential graph algorithms as a whole by following the *PIE* programming model from [GRAPE](https://dl.acm.org/doi/10.1145/3035918.3035942). Sequential algorithms can be easily ["plugged into"](examples/analytical_apps/sssp/sssp_auto.h) libgrape-lite with only minor changes and get parallelized to handle large graphs efficiently. In addition to the ease of programming, libgrape-lite is designed to be highly [efficient](Performance.md) and [flexible](examples/gnn_sampler), to cope the scale, variety and complexity from real-life graph applications.
 
@@ -101,6 +102,10 @@ The analytical applications support the LDBC Analytical Benchmark suite with the
 
 In addition to offline graph analytics, libgrape-lite could also be utilized to handle more complex graph tasks. A sampler for GNN training/inference on dynamic graphs (taking graph changes and queries, and producing results via [Kafka](https://kafka.apache.org/)) is included as an example. Please refer to [examples/gnn_sampler](./examples/gnn_sampler) for more details.
 
+### GPU-based graph analytics
+
+libgrape-lite also supports graph analytics on multi-GPU servers. Unlike CPUs, GPUs have more-but-weaker cores, making load balancing the key to high-performance sparse graph processing on GPUs. libgrape-lite provides multiple load balancing strategies on GPUs (`wm`, `cm`, `cta`, and `strict`). libgrape-lite adopts NCCL to handle communication between multiple GPUs. With GPU acceleration, libgrape-lite can obtain similar performance for a 4-node CPU cluster with a single GPU. The detailed benchmark results of libgrape-lite on GPUs could also be found [here](Performance.md).
+
 ## Documentation
 
 Documentation is generated using Doxygen. Users can build doxygen documentation in the build directory using: